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Kumar S, Bhowmik B. EffiCOVID-net: A highly efficient convolutional neural network for COVID-19 diagnosis using chest X-ray imaging. Methods 2025; 240:81-100. [PMID: 40252941 DOI: 10.1016/j.ymeth.2025.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2024] [Revised: 04/04/2025] [Accepted: 04/14/2025] [Indexed: 04/21/2025] Open
Abstract
The global COVID-19 pandemic has drastically affected daily life, emphasizing the urgent need for early and accurate detection to provide adequate medical treatment, especially with limited antiviral options. Chest X-ray imaging has proven crucial for distinguishing COVID-19 from other respiratory conditions, providing an essential diagnostic tool. Deep learning (DL)-based models have proven highly effective in image diagnostics in recent years. Many of these models are computationally intensive and prone to overfitting, especially when trained on limited datasets. Additionally, conventional models often fail to capture multi-scale features, reducing diagnostic accuracy. This paper proposed a highly efficient convolutional neural network (CNN) called EffiCOVID-Net, incorporating diverse feature learning units. The proposed model consists of a bunch of EffiCOVID blocks that incorporate several layers of convolution containing (3×3) filters and recurrent connections to extract complex features while preserving spatial integrity. The performance of EffiCOVID-Net is rigorously evaluated using standard performance metrics on two publicly available COVID-19 chest X-ray datasets. Experimental results demonstrate that EffiCOVID-Net outperforms existing models, achieving 98.68% accuracy on the COVID-19 radiography dataset (D1), 98.55% on the curated chest X-ray dataset (D2), and 98.87% on the mixed dataset (DMix) in multi-class classification (COVID-19 vs. Normal vs. Pneumonia). For binary classification (COVID-19 vs. Normal), the model attains 99.06%, 99.78%, and 99.07% accuracy, respectively. Integrating Grad-CAM-based visualizations further enhances interpretability by highlighting critical regions influencing model predictions. EffiCOVID-Net's lightweight architecture ensures low computational overhead, making it suitable for deployment in resource-constrained clinical settings. A comparative analysis with existing methods highlights its superior accuracy, efficiency, and robustness performance. However, while the model enhances diagnostic workflows, it is best utilized as an assistive tool rather than a standalone diagnostic method.
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Affiliation(s)
- Sunil Kumar
- Maharshi Patanjali CPS Lab, BRICS Laboratory, Department of Computer Science and Engineering, National Institute of Technology Karnataka, Mangalore 575025, Karnataka, India.
| | - Biswajit Bhowmik
- Maharshi Patanjali CPS Lab, BRICS Laboratory, Department of Computer Science and Engineering, National Institute of Technology Karnataka, Mangalore 575025, Karnataka, India
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Lella KK, Jagadeesh MS, Alphonse PJA. Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN. Health Inf Sci Syst 2024; 12:22. [PMID: 38469455 PMCID: PMC10924857 DOI: 10.1007/s13755-024-00283-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 02/21/2024] [Indexed: 03/13/2024] Open
Abstract
The utilization of lung sounds to diagnose lung diseases using respiratory sound features has significantly increased in the past few years. The Digital Stethoscope data has been examined extensively by medical researchers and technical scientists to diagnose the symptoms of respiratory diseases. Artificial intelligence-based approaches are applied in the real universe to distinguish respiratory disease signs from human pulmonary auscultation sounds. The Deep CNN model is implemented with combined multi-feature channels (Modified MFCC, Log Mel, and Soft Mel) to obtain the sound parameters from lung-based Digital Stethoscope data. The model analysis is observed with max-pooling and without max-pool operations using multi-feature channels on respiratory digital stethoscope data. In addition, COVID-19 sound data and enriched data, which are recently acquired data to enhance model performance using a combination of L2 regularization to overcome the risk of overfitting because of less respiratory sound data, are included in the work. The suggested DCNN with Max-Pooling on the improved dataset demonstrates cutting-edge performance employing a multi-feature channels spectrogram. The model has been developed with different convolutional filter sizes (1 × 12 , 1 × 24 , 1 × 36 , 1 × 48 , and 1 × 60 ) that helped to test the proposed neural network. According to the experimental findings, the suggested DCNN architecture with a max-pooling function performs better to identify respiratory disease symptoms than DCNN without max-pooling. In order to demonstrate the model's effectiveness in categorization, it is trained and tested with the DCNN model that extract several modalities of respiratory sound data.
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Affiliation(s)
- Kranthi Kumar Lella
- School of Computer Science and Engineering, VIT-AP University, Vijayawada, Guntur, Andhra Pradesh 522237 India
| | - M. S. Jagadeesh
- School of Computer Science and Engineering, VIT-AP University, Vijayawada, Guntur, Andhra Pradesh 522237 India
| | - P. J. A. Alphonse
- Department of Computer Applications, NIT Tiruchirappalli, Tiruchirappalli, Guntur, Tamil Nadu 620015 India
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Tang T, Zhang R. A Multi-Task Model for Pulmonary Nodule Segmentation and Classification. J Imaging 2024; 10:234. [PMID: 39330454 PMCID: PMC11433280 DOI: 10.3390/jimaging10090234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2024] [Revised: 09/16/2024] [Accepted: 09/18/2024] [Indexed: 09/28/2024] Open
Abstract
In the computer-aided diagnosis of lung cancer, the automatic segmentation of pulmonary nodules and the classification of benign and malignant tumors are two fundamental tasks. However, deep learning models often overlook the potential benefits of task correlations in improving their respective performances, as they are typically designed for a single task only. Therefore, we propose a multi-task network (MT-Net) that integrates shared backbone architecture and a prediction distillation structure for the simultaneous segmentation and classification of pulmonary nodules. The model comprises a coarse segmentation subnetwork (Coarse Seg-net), a cooperative classification subnetwork (Class-net), and a cooperative segmentation subnetwork (Fine Seg-net). Coarse Seg-net and Fine Seg-net share identical structure, where Coarse Seg-net provides prior location information for the subsequent Fine Seg-net and Class-net, thereby boosting pulmonary nodule segmentation and classification performance. We quantitatively and qualitatively analyzed the performance of the model by using the public dataset LIDC-IDRI. Our results show that the model achieves a Dice similarity coefficient (DI) index of 83.2% for pulmonary nodule segmentation, as well as an accuracy (ACC) of 91.9% for benign and malignant pulmonary nodule classification, which is competitive with other state-of-the-art methods. The experimental results demonstrate that the performance of pulmonary nodule segmentation and classification can be improved by a unified model that leverages the potential correlation between tasks.
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Affiliation(s)
- Tiequn Tang
- School of Physics and Electronic Engineering, Fuyang Normal University, Fuyang 236037, China
| | - Rongfu Zhang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China
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Siddiqi R, Javaid S. Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey. J Imaging 2024; 10:176. [PMID: 39194965 DOI: 10.3390/jimaging10080176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/15/2024] [Accepted: 07/19/2024] [Indexed: 08/29/2024] Open
Abstract
This paper addresses the significant problem of identifying the relevant background and contextual literature related to deep learning (DL) as an evolving technology in order to provide a comprehensive analysis of the application of DL to the specific problem of pneumonia detection via chest X-ray (CXR) imaging, which is the most common and cost-effective imaging technique available worldwide for pneumonia diagnosis. This paper in particular addresses the key period associated with COVID-19, 2020-2023, to explain, analyze, and systematically evaluate the limitations of approaches and determine their relative levels of effectiveness. The context in which DL is applied as both an aid to and an automated substitute for existing expert radiography professionals, who often have limited availability, is elaborated in detail. The rationale for the undertaken research is provided, along with a justification of the resources adopted and their relevance. This explanatory text and the subsequent analyses are intended to provide sufficient detail of the problem being addressed, existing solutions, and the limitations of these, ranging in detail from the specific to the more general. Indeed, our analysis and evaluation agree with the generally held view that the use of transformers, specifically, vision transformers (ViTs), is the most promising technique for obtaining further effective results in the area of pneumonia detection using CXR images. However, ViTs require extensive further research to address several limitations, specifically the following: biased CXR datasets, data and code availability, the ease with which a model can be explained, systematic methods of accurate model comparison, the notion of class imbalance in CXR datasets, and the possibility of adversarial attacks, the latter of which remains an area of fundamental research.
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Affiliation(s)
- Raheel Siddiqi
- Computer Science Department, Karachi Campus, Bahria University, Karachi 73500, Pakistan
| | - Sameena Javaid
- Computer Science Department, Karachi Campus, Bahria University, Karachi 73500, Pakistan
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Yenikaya MA, Kerse G, Oktaysoy O. Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images. Front Public Health 2024; 12:1386110. [PMID: 38660365 PMCID: PMC11039909 DOI: 10.3389/fpubh.2024.1386110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/02/2024] [Indexed: 04/26/2024] Open
Abstract
Purpose Artificial intelligence has led to significant developments in the healthcare sector, as in other sectors and fields. In light of its significance, the present study delves into exploring deep learning, a branch of artificial intelligence. Methods In the study, deep learning networks ResNet101, AlexNet, GoogLeNet, and Xception were considered, and it was aimed to determine the success of these networks in disease diagnosis. For this purpose, a dataset of 1,680 chest X-ray images was utilized, consisting of cases of COVID-19, viral pneumonia, and individuals without these diseases. These images were obtained by employing a rotation method to generate replicated data, wherein a split of 70 and 30% was adopted for training and validation, respectively. Results The analysis findings revealed that the deep learning networks were successful in classifying COVID-19, Viral Pneumonia, and Normal (disease-free) images. Moreover, an examination of the success levels revealed that the ResNet101 deep learning network was more successful than the others with a 96.32% success rate. Conclusion In the study, it was seen that deep learning can be used in disease diagnosis and can help experts in the relevant field, ultimately contributing to healthcare organizations and the practices of country managers.
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Affiliation(s)
| | - Gökhan Kerse
- Faculty of Economics and Administrative Sciences, Department of Management Information Systems, Kafkas University, Kars, Türkiye
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Kaur BP, Singh H, Hans R, Sharma SK, Kaushal C, Hassan MM, Shah MA. An augmentation aided concise CNN based architecture for COVID-19 diagnosis in real time. Sci Rep 2024; 14:1136. [PMID: 38212647 PMCID: PMC10784465 DOI: 10.1038/s41598-024-51317-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 01/03/2024] [Indexed: 01/13/2024] Open
Abstract
Over 6.5 million people around the world have lost their lives due to the highly contagious COVID 19 virus. The virus increases the danger of fatal health effects by damaging the lungs severely. The only method to reduce mortality and contain the spread of this disease is by promptly detecting it. Recently, deep learning has become one of the most prominent approaches to CAD, helping surgeons make more informed decisions. But deep learning models are computation hungry and devices with TPUs and GPUs are needed to run these models. The current focus of machine learning research is on developing models that can be deployed on mobile and edge devices. To this end, this research aims to develop a concise convolutional neural network-based computer-aided diagnostic system for detecting the COVID 19 virus in X-ray images, which may be deployed on devices with limited processing resources, such as mobile phones and tablets. The proposed architecture aspires to use the image enhancement in first phase and data augmentation in the second phase for image pre-processing, additionally hyperparameters are also optimized to obtain the optimal parameter settings in the third phase that provide the best results. The experimental analysis has provided empirical evidence of the impact of image enhancement, data augmentation, and hyperparameter tuning on the proposed convolutional neural network model, which increased accuracy from 94 to 98%. Results from the evaluation show that the suggested method gives an accuracy of 98%, which is better than popular transfer learning models like Xception, Resnet50, and Inception.
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Affiliation(s)
- Balraj Preet Kaur
- Department of Computer Science and Engineering, DAV University, Jalandhar, India
| | - Harpreet Singh
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India
| | - Rahul Hans
- Department of Computer Science and Engineering, DAV University, Jalandhar, India
| | - Sanjeev Kumar Sharma
- Department of Computer Science and Applications, DAV University, Jalandhar, India
| | - Chetna Kaushal
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, 140401, India
| | - Md Mehedi Hassan
- Computer Science and Engineering Discipline, Khulna University, Khulna, 9208, Bangladesh
| | - Mohd Asif Shah
- Department of Economics, Kebri Dehar University, Kebri Dehar, 250, Ethiopia.
- Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.
- Division of Research and Development, Lovely Professional University, Phagwara, 144001, Punjab, India.
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Park S, Kim JH, Ahn Y, Lee CH, Kim YG, Yuh WT, Hyun SJ, Kim CH, Kim KJ, Chung CK. Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis. Sci Rep 2024; 14:203. [PMID: 38168665 PMCID: PMC10761871 DOI: 10.1038/s41598-023-50885-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 12/27/2023] [Indexed: 01/05/2024] Open
Abstract
Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing LSS. In this study, we aimed to improve and generalize the performance of CNN models and overcome the limitation of the single-pose-based CNN (SP-CNN) model using multi-pose radiographs. Individuals with severe or no LSS, confirmed using MRI, were enrolled. Lateral radiographs of patients in three postures were collected. We developed a multi-pose-based CNN (MP-CNN) model using the encoders of the three SP-CNN model (extension, flexion, and neutral postures). We compared the validation results of the MP-CNN model using four algorithms pretrained with ImageNet. The MP-CNN model underwent additional internal and external validations to measure generalization performance. The ResNet50-based MP-CNN model achieved the largest area under the receiver operating characteristic curve (AUROC) of 91.4% (95% confidence interval [CI] 90.9-91.8%) for internal validation. The AUROC of the MP-CNN model were 91.3% (95% CI 90.7-91.9%) and 79.5% (95% CI 78.2-80.8%) for the extra-internal and external validation, respectively. The MP-CNN based heatmap offered a logical decision-making direction through optimized visualization. This model holds potential as a screening tool for LSS diagnosis, offering an explainable rationale for its prediction.
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Affiliation(s)
- Seyeon Park
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, 101 Daehak-Ro, Jongro-Gu, Seoul, 03080, Republic of Korea
| | - Jun-Hoe Kim
- Department of Neurosurgery, Seoul National University Hospital, 101 Daehak-Ro, Jongro-Gu, Seoul, 03080, Republic of Korea
| | - Youngbin Ahn
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, 101 Daehak-Ro, Jongro-Gu, Seoul, 03080, Republic of Korea
| | - Chang-Hyun Lee
- Department of Neurosurgery, Seoul National University Hospital, 101 Daehak-Ro, Jongro-Gu, Seoul, 03080, Republic of Korea.
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Young-Gon Kim
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, 101 Daehak-Ro, Jongro-Gu, Seoul, 03080, Republic of Korea.
| | - Woon Tak Yuh
- Department of Neurosurgery, Seoul National University Hospital, 101 Daehak-Ro, Jongro-Gu, Seoul, 03080, Republic of Korea
| | - Seung-Jae Hyun
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Chi Heon Kim
- Department of Neurosurgery, Seoul National University Hospital, 101 Daehak-Ro, Jongro-Gu, Seoul, 03080, Republic of Korea
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ki-Jeong Kim
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Chun Kee Chung
- Department of Neurosurgery, Seoul National University Hospital, 101 Daehak-Ro, Jongro-Gu, Seoul, 03080, Republic of Korea
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
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Samariya D, Ma J, Aryal S, Zhao X. Detection and explanation of anomalies in healthcare data. Health Inf Sci Syst 2023; 11:20. [PMID: 37035724 PMCID: PMC10079801 DOI: 10.1007/s13755-023-00221-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 02/25/2023] [Indexed: 04/09/2023] Open
Abstract
The growth of databases in the healthcare domain opens multiple doors for machine learning and artificial intelligence technology. Many medical devices are available in the medical field; however, medical errors remain a severe challenge. Different algorithms are developed to identify and solve medical errors, such as detecting anomalous readings, anomalous health conditions of a patient, etc. However, they fail to answer why those entries are considered an anomaly. This research gap leads to an outlying aspect mining problem. The problem of outlying aspect mining aims to discover the set of features (a.k.a subspace) in which the given data point is dramatically different than others. In this paper, we present a framework that detects anomalies in healthcare data and then provides an explanation of anomalies. This paper aims to effectively and efficiently detect anomalies and explain why they are considered anomalies by detecting outlying aspects. First, we re-introduced four anomaly detection techniques and outlying aspect mining algorithms. Then, we evaluate the performance of anomaly detection techniques and choose the best anomaly detection algorithm. Later, we detect the top k anomaly as a query and detect their outlying aspect. Lastly, we evaluate their performance on 16 real-world healthcare datasets. The experimental results show that the latest isolation-based outlying aspect mining measure, SiNNE, has outstanding performance on this task and has promising results.
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Affiliation(s)
- Durgesh Samariya
- Institute of Innovation, Science and Sustainability, Federation University, Berwick, VIC Australia
| | - Jiangang Ma
- Institute of Innovation, Science and Sustainability, Federation University, Berwick, VIC Australia
| | - Sunil Aryal
- School of Information Technology, Deakin University, Geelong, VIC Australia
| | - Xiaohui Zhao
- Institute of Innovation, Science and Sustainability, Federation University, Ballarat, VIC Australia
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Pham TD. Prediction of Five-Year Survival Rate for Rectal Cancer Using Markov Models of Convolutional Features of RhoB Expression on Tissue Microarray. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3195-3204. [PMID: 37155403 DOI: 10.1109/tcbb.2023.3274211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The ability to predict survival in cancer is clinically important because the finding can help patients and physicians make optimal treatment decisions. Artificial intelligence in the context of deep learning has been increasingly realized by the informatics-oriented medical community as a powerful machine-learning technology for cancer research, diagnosis, prediction, and treatment. This paper presents the combination of deep learning, data coding, and probabilistic modeling for predicting five-year survival in a cohort of patients with rectal cancer using images of RhoB expression on biopsies. Using about one-third of the patients' data for testing, the proposed approach achieved 90% prediction accuracy, which is much higher than the direct use of the best pretrained convolutional neural network (70%) and the best coupling of a pretrained model and support vector machines (70%).
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Taşyürek M, Öztürk C. A fine-tuned YOLOv5 deep learning approach for real-time house number detection. PeerJ Comput Sci 2023; 9:e1453. [PMID: 37547390 PMCID: PMC10403189 DOI: 10.7717/peerj-cs.1453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/01/2023] [Indexed: 08/08/2023]
Abstract
Detection of small objects in natural scene images is a complicated problem due to the blur and depth found in the images. Detecting house numbers from the natural scene images in real-time is a computer vision problem. On the other hand, convolutional neural network (CNN) based deep learning methods have been widely used in object detection in recent years. In this study, firstly, a classical CNN-based approach is used to detect house numbers with locations from natural images in real-time. Faster R-CNN, MobileNet, YOLOv4, YOLOv5 and YOLOv7, among the commonly used CNN models, models were applied. However, satisfactory results could not be obtained due to the small size and variable depth of the door plate objects. A new approach using the fine-tuning technique is proposed to improve the performance of CNN-based deep learning models. Experimental evaluations were made on real data from Kayseri province. Classic Faster R-CNN, MobileNet, YOLOv4, YOLOv5 and YOLOv7 methods yield f1 scores of 0.763, 0.677, 0.880, 0.943 and 0.842, respectively. The proposed fine-tuned Faster R-CNN, MobileNet, YOLOv4, YOLOv5, and YOLOv7 approaches achieved f1 scores of 0.845, 0.775, 0.932, 0.972 and 0.889, respectively. Thanks to the proposed fine-tuned approach, the f1 score of all models has increased. Regarding the run time of the methods, classic Faster R-CNN detects 0.603 seconds, while fine-tuned Faster R-CNN detects 0.633 seconds. Classic MobileNet detects 0.046 seconds, while fine-tuned MobileNet detects 0.048 seconds. Classic YOLOv4 and fine-tuned YOLOv4 detect 0.235 and 0.240 seconds, respectively. Classic YOLOv5 and fine-tuned YOLOv5 detect 0.015 seconds, and classic YOLOv7 and fine-tuned YOLOv7 detect objects in 0.009 seconds. While the YOLOv7 model was the fastest running model with an average running time of 0.009 seconds, the proposed fine-tuned YOLOv5 approach achieved the highest performance with an f1 score of 0.972.
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Affiliation(s)
- Murat Taşyürek
- Department of Computer Engineering, Kayseri University, Kayseri, Turkey
| | - Celal Öztürk
- Department of Computer Engineering, Erciyes University, Kayseri, Turkey
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Varde AS, Karthikeyan D, Wang W. Facilitating COVID recognition from X-rays with computer vision models and transfer learning. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 83:1-32. [PMID: 37362714 PMCID: PMC10213594 DOI: 10.1007/s11042-023-15744-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 05/17/2022] [Accepted: 04/22/2023] [Indexed: 06/28/2023]
Abstract
Multimedia data plays an important role in medicine and healthcare since EHR (Electronic Health Records) entail complex images and videos for analyzing patient data. In this article, we hypothesize that transfer learning with computer vision can be adequately harnessed on such data, more specifically chest X-rays, to learn from a few images for assisting accurate, efficient recognition of COVID. While researchers have analyzed medical data (including COVID data) using computer vision models, the main contributions of our study entail the following. Firstly, we conduct transfer learning using a few images from publicly available big data on chest X-rays, suitably adapting computer vision models with data augmentation. Secondly, we aim to find the best fit models to solve this problem, adjusting the number of samples for training and validation to obtain the minimum number of samples with maximum accuracy. Thirdly, our results indicate that combining chest radiography with transfer learning has the potential to improve the accuracy and timeliness of radiological interpretations of COVID in a cost-effective manner. Finally, we outline applications of this work during COVID and its recovery phases with future issues for research and development. This research exemplifies the use of multimedia technology and machine learning in healthcare.
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Affiliation(s)
- Aparna S. Varde
- School of Computing, Montclair State University, Montclair, NJ USA
- Max Planck Institute for Informatics (Visiting Researcher), Saarbrucken, Germany
| | | | - Weitian Wang
- School of Computing, Montclair State University, Montclair, NJ USA
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12
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Chakraborty GS, Batra S, Singh A, Muhammad G, Torres VY, Mahajan M. A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling. Diagnostics (Basel) 2023; 13:diagnostics13101806. [PMID: 37238290 DOI: 10.3390/diagnostics13101806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023] Open
Abstract
COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient's life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.
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Affiliation(s)
- Gouri Shankar Chakraborty
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Salil Batra
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Aman Singh
- Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
- Department of Engineering, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
- Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Vanessa Yelamos Torres
- Department of Engineering, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
- Engineering Research & Innovation Group, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche C.P. 24560, Mexico
| | - Makul Mahajan
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
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Lee MH, Shomanov A, Kudaibergenova M, Viderman D. Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review. J Clin Med 2023; 12:jcm12103446. [PMID: 37240552 DOI: 10.3390/jcm12103446] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/25/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
SARS-CoV-2 is a novel virus that has been affecting the global population by spreading rapidly and causing severe complications, which require prompt and elaborate emergency treatment. Automatic tools to diagnose COVID-19 could potentially be an important and useful aid. Radiologists and clinicians could potentially rely on interpretable AI technologies to address the diagnosis and monitoring of COVID-19 patients. This paper aims to provide a comprehensive analysis of the state-of-the-art deep learning techniques for COVID-19 classification. The previous studies are methodically evaluated, and a summary of the proposed convolutional neural network (CNN)-based classification approaches is presented. The reviewed papers have presented a variety of CNN models and architectures that were developed to provide an accurate and quick automatic tool to diagnose the COVID-19 virus based on presented CT scan or X-ray images. In this systematic review, we focused on the critical components of the deep learning approach, such as network architecture, model complexity, parameter optimization, explainability, and dataset/code availability. The literature search yielded a large number of studies over the past period of the virus spread, and we summarized their past efforts. State-of-the-art CNN architectures, with their strengths and weaknesses, are discussed with respect to diverse technical and clinical evaluation metrics to safely implement current AI studies in medical practice.
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Affiliation(s)
- Min-Ho Lee
- School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan
| | - Adai Shomanov
- School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan
| | - Madina Kudaibergenova
- School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan
| | - Dmitriy Viderman
- School of Medicine, Nazarbayev University, 5/1 Kerey and Zhanibek Khandar Str., Astana 010000, Kazakhstan
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14
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Mavragani A, Wongsirichot T, Damkliang K, Navasakulpong A. Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation. JMIR Form Res 2023; 7:e42324. [PMID: 36780315 PMCID: PMC9976774 DOI: 10.2196/42324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 02/10/2023] [Accepted: 02/13/2023] [Indexed: 02/14/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has raised global concern, with moderate to severe cases displaying lung inflammation and respiratory failure. Chest x-ray (CXR) imaging is crucial for diagnosis and is usually interpreted by experienced medical specialists. Machine learning has been applied with acceptable accuracy, but computational efficiency has received less attention. OBJECTIVE We introduced a novel hybrid machine learning model to accurately classify COVID-19, non-COVID-19, and healthy patients from CXR images with reduced computational time and promising results. Our proposed model was thoroughly evaluated and compared with existing models. METHODS A retrospective study was conducted to analyze 5 public data sets containing 4200 CXR images using machine learning techniques including decision trees, support vector machines, and neural networks. The images were preprocessed to undergo image segmentation, enhancement, and feature extraction. The best performing machine learning technique was selected and combined into a multilayer hybrid classification model for COVID-19 (MLHC-COVID-19). The model consisted of 2 layers. The first layer was designed to differentiate healthy individuals from infected patients, while the second layer aimed to classify COVID-19 and non-COVID-19 patients. RESULTS The MLHC-COVID-19 model was trained and evaluated on unseen COVID-19 CXR images, achieving reasonably high accuracy and F measures of 0.962 and 0.962, respectively. These results show the effectiveness of the MLHC-COVID-19 in classifying COVID-19 CXR images, with improved accuracy and a reduction in interpretation time. The model was also embedded into a web-based MLHC-COVID-19 computer-aided diagnosis system, which was made publicly available. CONCLUSIONS The study found that the MLHC-COVID-19 model effectively differentiated CXR images of COVID-19 patients from those of healthy and non-COVID-19 individuals. It outperformed other state-of-the-art deep learning techniques and showed promising results. These results suggest that the MLHC-COVID-19 model could have been instrumental in early detection and diagnosis of COVID-19 patients, thus playing a significant role in controlling and managing the pandemic. Although the pandemic has slowed down, this model can be adapted and utilized for future similar situations. The model was also integrated into a publicly accessible web-based computer-aided diagnosis system.
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Affiliation(s)
| | - Thakerng Wongsirichot
- Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand
| | - Kasikrit Damkliang
- Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand
| | - Asma Navasakulpong
- Division of Respiratory and Respiratory Critical Care Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
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15
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Pham TD, Ravi V, Luo B, Fan C, Sun XF. Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:1-16. [PMID: 36937315 PMCID: PMC10017185 DOI: 10.37349/etat.2023.00119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 10/31/2022] [Indexed: 02/10/2023] Open
Abstract
Aim The process of biomarker discovery is being accelerated with the application of artificial intelligence (AI), including machine learning. Biomarkers of diseases are useful because they are indicators of pathogenesis or measures of responses to therapeutic treatments, and therefore, play a key role in new drug development. Proteins are among the candidates for biomarkers of rectal cancer, which need to be explored using state-of-the-art AI to be utilized for prediction, prognosis, and therapeutic treatment. This paper aims to investigate the predictive power of Ras homolog family member B (RhoB) protein in rectal cancer. Methods This study introduces the integration of pretrained convolutional neural networks and support vector machines (SVMs) for classifying biopsy samples of immunohistochemical expression of protein RhoB in rectal-cancer patients to validate its biologic measure in biopsy. Features of the immunohistochemical expression images were extracted by the pretrained networks and used for binary classification by the SVMs into two groups of less and more than 5-year survival rates. Results The fusion of neural search architecture network (NASNet)-Large for deep-layer feature extraction and classifier using SVMs provided the best average classification performance with a total accuracy = 85%, prediction of survival rate of more than 5 years = 90%, and prediction of survival rate of less than 5 years = 75%. Conclusions The finding obtained from the use of AI reported in this study suggest that RhoB expression on rectal-cancer biopsy can be potentially used as a biomarker for predicting survival outcomes in rectal-cancer patients, which can be informative for clinical decision making if the patient would be recommended for preoperative therapy.
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Affiliation(s)
- Tuan D. Pham
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar 34754, Saudi Arabia
| | - Vinayakumar Ravi
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar 34754, Saudi Arabia
| | - Bin Luo
- Department of Biomedical and Clinical Sciences, Linköping University, 58183 Linköping, Sweden
- Department of Gastrointestinal Surgery, Sichuan Provincial People’s Hospital, Chengdu 610032, Sichuan, China
| | - Chuanwen Fan
- Department of Biomedical and Clinical Sciences, Linköping University, 58183 Linköping, Sweden
| | - Xiao-Feng Sun
- Department of Biomedical and Clinical Sciences, Linköping University, 58183 Linköping, Sweden
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16
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Chen X, Bai Y, Wang P, Luo J. Data augmentation based semi-supervised method to improve COVID-19 CT classification. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:6838-6852. [PMID: 37161130 DOI: 10.3934/mbe.2023294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The Coronavirus (COVID-19) outbreak of December 2019 has become a serious threat to people around the world, creating a health crisis that infected millions of lives, as well as destroying the global economy. Early detection and diagnosis are essential to prevent further transmission. The detection of COVID-19 computed tomography images is one of the important approaches to rapid diagnosis. Many different branches of deep learning methods have played an important role in this area, including transfer learning, contrastive learning, ensemble strategy, etc. However, these works require a large number of samples of expensive manual labels, so in order to save costs, scholars adopted semi-supervised learning that applies only a few labels to classify COVID-19 CT images. Nevertheless, the existing semi-supervised methods focus primarily on class imbalance and pseudo-label filtering rather than on pseudo-label generation. Accordingly, in this paper, we organized a semi-supervised classification framework based on data augmentation to classify the CT images of COVID-19. We revised the classic teacher-student framework and introduced the popular data augmentation method Mixup, which widened the distribution of high confidence to improve the accuracy of selected pseudo-labels and ultimately obtain a model with better performance. For the COVID-CT dataset, our method makes precision, F1 score, accuracy and specificity 21.04%, 12.95%, 17.13% and 38.29% higher than average values for other methods respectively, For the SARS-COV-2 dataset, these increases were 8.40%, 7.59%, 9.35% and 12.80% respectively. For the Harvard Dataverse dataset, growth was 17.64%, 18.89%, 19.81% and 20.20% respectively. The codes are available at https://github.com/YutingBai99/COVID-19-SSL.
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Affiliation(s)
- Xiangtao Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
| | - Yuting Bai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
| | - Peng Wang
- College of Computer Science and Engineering, Hunan Institute of Technology, Hengyang 421002, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
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17
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Suba S, Muthulakshmi M. A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques. NETWORK (BRISTOL, ENGLAND) 2023; 34:26-64. [PMID: 36420865 DOI: 10.1080/0954898x.2022.2147231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/27/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.
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Affiliation(s)
- Saravanan Suba
- Department of Computer Science, Kamarajar Government Arts College, Tirunelveli, Surandai 627859, India
| | - M Muthulakshmi
- Department of Computer Science, Kamarajar Government Arts College, Tirunelveli, Surandai 627859, India
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18
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Aldhahi W, Sull S. Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability. Diagnostics (Basel) 2023; 13:441. [PMID: 36766546 PMCID: PMC9914375 DOI: 10.3390/diagnostics13030441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/08/2023] [Accepted: 01/20/2023] [Indexed: 01/27/2023] Open
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on patients and healthcare systems across the world. Distinguishing non-COVID-19 patients from COVID-19 patients at the lowest possible cost and in the earliest stages of the disease is a major issue. Additionally, the implementation of explainable deep learning decisions is another issue, especially in critical fields such as medicine. The study presents a method to train deep learning models and apply an uncertainty-based ensemble voting policy to achieve 99% accuracy in classifying COVID-19 chest X-rays from normal and pneumonia-related infections. We further present a training scheme that integrates the cyclic cosine annealing approach with cross-validation and uncertainty quantification that is measured using prediction interval coverage probability (PICP) as final ensemble voting weights. We also propose the Uncertain-CAM technique, which improves deep learning explainability and provides a more reliable COVID-19 classification system. We introduce a new image processing technique to measure the explainability based on ground-truth, and we compared it with the widely adopted Grad-CAM method.
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Affiliation(s)
| | - Sanghoon Sull
- School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
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19
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Khan A, Khan SH, Saif M, Batool A, Sohail A, Waleed Khan M. A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2165724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Asifullah Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan
- Center for Mathematical Sciences, Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan
| | - Saddam Hussain Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat, Pakistan
| | - Mahrukh Saif
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
| | - Asiya Batool
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
| | - Anabia Sohail
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Computer Science, Faculty of Computing & Artificial Intelligence, Air University, Islamabad, Pakistan
| | - Muhammad Waleed Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Mechanical and Aerospace Engineering, Columbus, OH, USA
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20
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Jyoti K, Sushma S, Yadav S, Kumar P, Pachori RB, Mukherjee S. Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images. Comput Biol Med 2023; 152:106331. [PMID: 36502692 PMCID: PMC9683525 DOI: 10.1016/j.compbiomed.2022.106331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/01/2022] [Accepted: 11/14/2022] [Indexed: 11/25/2022]
Abstract
In this era of Coronavirus disease 2019 (COVID-19), an accurate method of diagnosis with less diagnosis time and cost can effectively help in controlling the disease spread with the new variants taking birth from time to time. In order to achieve this, a two-dimensional (2D) tunable Q-wavelet transform (TQWT) based on a memristive crossbar array (MCA) is introduced in this work for the decomposition of chest X-ray images of two different datasets. TQWT has resulted in promising values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) at the optimum values of its parameters namely quality factor (Q) of 4, and oversampling rate (r) of 3 and at a decomposition level (J) of 2. The MCA-based model is used to process decomposed images for further classification with efficient storage. These images have been further used for the classification of COVID-19 and non-COVID-19 images using ResNet50 and AlexNet convolutional neural network (CNN) models. The average accuracy values achieved for the processed chest X-ray images classification in the small and large datasets are 98.82% and 94.64%, respectively which are higher than the reported conventional methods based on different models of deep learning techniques. The average accuracy of detection of COVID-19 via the proposed method of image classification has also been achieved with less complexity, energy, power, and area consumption along with lower cost estimation as compared to CMOS-based technology.
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Affiliation(s)
- Kumari Jyoti
- Hybrid Nanodevice Research Group (HNRG), Department of Electrical Engineering, Indian Institute of Technology Indore, Madhya Pradesh, 453552, India
| | - Sai Sushma
- Hybrid Nanodevice Research Group (HNRG), Department of Electrical Engineering, Indian Institute of Technology Indore, Madhya Pradesh, 453552, India
| | - Saurabh Yadav
- Hybrid Nanodevice Research Group (HNRG), Centre for Advanced Electronics (CAE), Indian Institute of Technology Indore, Madhya Pradesh, 453552, India
| | - Pawan Kumar
- Hybrid Nanodevice Research Group (HNRG), Department of Electrical Engineering, Indian Institute of Technology Indore, Madhya Pradesh, 453552, India
| | - Ram Bilas Pachori
- Department of Electrical Engineering, Indian Institute of Technology Indore, Madhya Pradesh, 453552, India
| | - Shaibal Mukherjee
- Hybrid Nanodevice Research Group (HNRG), Department of Electrical Engineering, Indian Institute of Technology Indore, Madhya Pradesh, 453552, India; Hybrid Nanodevice Research Group (HNRG), Centre for Advanced Electronics (CAE), Indian Institute of Technology Indore, Madhya Pradesh, 453552, India; Centre for Rural Development and Technology (CRDT), Indian Institute of Technology Indore, Madhya Pradesh, 453552, India; School of Engineering, RMIT University, Melbourne, Victoria, 3001, Australia.
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21
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Akhter Y, Singh R, Vatsa M. AI-based radiodiagnosis using chest X-rays: A review. Front Big Data 2023; 6:1120989. [PMID: 37091458 PMCID: PMC10116151 DOI: 10.3389/fdata.2023.1120989] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 01/06/2023] [Indexed: 04/25/2023] Open
Abstract
Chest Radiograph or Chest X-ray (CXR) is a common, fast, non-invasive, relatively cheap radiological examination method in medical sciences. CXRs can aid in diagnosing many lung ailments such as Pneumonia, Tuberculosis, Pneumoconiosis, COVID-19, and lung cancer. Apart from other radiological examinations, every year, 2 billion CXRs are performed worldwide. However, the availability of the workforce to handle this amount of workload in hospitals is cumbersome, particularly in developing and low-income nations. Recent advances in AI, particularly in computer vision, have drawn attention to solving challenging medical image analysis problems. Healthcare is one of the areas where AI/ML-based assistive screening/diagnostic aid can play a crucial part in social welfare. However, it faces multiple challenges, such as small sample space, data privacy, poor quality samples, adversarial attacks and most importantly, the model interpretability for reliability on machine intelligence. This paper provides a structured review of the CXR-based analysis for different tasks, lung diseases and, in particular, the challenges faced by AI/ML-based systems for diagnosis. Further, we provide an overview of existing datasets, evaluation metrics for different[][15mm][0mm]Q5 tasks and patents issued. We also present key challenges and open problems in this research domain.
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22
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Cao Z, Huang J, He X, Zong Z. BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images. Knowl Based Syst 2022; 258:110040. [PMID: 36284666 PMCID: PMC9585896 DOI: 10.1016/j.knosys.2022.110040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/09/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022]
Abstract
During the past two years, a highly infectious virus known as COVID-19 has been damaging and harming the health of people all over the world. Simultaneously, the number of patients is rising in various countries, with many new cases appearing daily, posing a significant challenge to hospital medical staff. It is necessary to improve the efficiency of virus detection. To this end, we combine modern technology and visual assistance to detect COVID-19. Based on the above facts, for accurate and rapid identification of infected persons, the BND-VGG-19 method was proposed. This method is based on VGG-19 and further incorporates batch normalization and dropout layers between the layers to improve network accuracy. Then, the COVID-19 dataset including viral pneumonia, COVID-19, and normal X-ray images, are used to diagnose lung abnormalities and test the performance of the proposed algorithm. The experimental results show the superiority of BND-VGG-19 with a 95.48% accuracy rate compared with existing COVID-19 diagnostic methods.
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Affiliation(s)
- Zili Cao
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China
| | - Junjian Huang
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China,Corresponding author
| | - Xing He
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China
| | - Zhaowen Zong
- Department of Training Base for Health Care, Army Medical University, Chongqing 400038, PR China
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23
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Pham TD, Ravi V, Fan C, Luo B, Sun XF. Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 11:87-95. [PMID: 36704244 PMCID: PMC9870269 DOI: 10.1109/jtehm.2022.3229561] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/06/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Over a decade, tissues dissected adjacent to primary tumors have been considered "normal" or healthy samples (NATs). However, NATs have recently been discovered to be distinct from both tumorous and normal tissues. The ability to predict the survival rate of cancer patients using NATs can open a new door to selecting optimal treatments for cancer and discovering biomarkers. METHODS This paper introduces an artificial intelligence (AI) approach that uses NATs for predicting the 5-year survival of pre-operative radiotherapy patients with rectal cancer. The new approach combines pre-trained deep learning, nonlinear dynamics, and long short-term memory to classify immunohistochemical images of RhoB protein expression on NATs. RESULTS Ten-fold cross-validation results show 88% accuracy of prediction obtained from the new approach, which is also higher than those provided from baseline methods. CONCLUSION Preliminary results not only add objective evidence to recent findings of NATs' molecular characteristics using state-of-the-art AI methods, but also contribute to the discovery of RhoB expression on NATs in rectal-cancer patients. CLINICAL IMPACT The ability to predict the survival rate of cancer patients is extremely important for clinical decision-making. The proposed AI tool is promising for assisting oncologists in their treatments of rectal cancer patients.
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Affiliation(s)
- Tuan D Pham
- Center for Artificial IntelligencePrince Mohammad Bin Fahd University Khobar 31952 Saudi Arabia
| | - Vinayakumar Ravi
- Center for Artificial IntelligencePrince Mohammad Bin Fahd University Khobar 31952 Saudi Arabia
| | - Chuanwen Fan
- Department of OncologyLinkoping University 58185 Linkoping Sweden
- Department of Biomedical and Clinical SciencesLinkoping University 58185 Linkoping Sweden
| | - Bin Luo
- Department of OncologyLinkoping University 58185 Linkoping Sweden
- Department of Biomedical and Clinical SciencesLinkoping University 58185 Linkoping Sweden
- Department of Gastrointestinal SurgerySichuan Provincial People's Hospital Chengdu 610032 China
| | - Xiao-Feng Sun
- Department of OncologyLinkoping University 58185 Linkoping Sweden
- Department of Biomedical and Clinical SciencesLinkoping University 58185 Linkoping Sweden
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24
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Lasker A, Obaidullah SM, Chakraborty C, Roy K. Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review. SN COMPUTER SCIENCE 2022; 4:65. [PMID: 36467853 PMCID: PMC9702883 DOI: 10.1007/s42979-022-01464-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 10/18/2022] [Indexed: 11/26/2022]
Abstract
Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.
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Affiliation(s)
- Asifuzzaman Lasker
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Sk Md Obaidullah
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Chandan Chakraborty
- Department of Computer Science & Engineering, National Institute of Technical Teachers’ Training & Research Kolkata, Kolkata, India
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Barasat, India
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25
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Ahila T, Subhajini AC. E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022; 116:105398. [PMID: 36158870 PMCID: PMC9485443 DOI: 10.1016/j.engappai.2022.105398] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/30/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
Background Recently, the coronavirus disease 2019 (COVID-19) has caused mortality of many people globally. Thus, there existed a need to detect this disease to prevent its further spread. Hence, the study aims to predict COVID-19 infected patients based on deep learning (DL) and image processing. Objectives The study intends to classify the normal and abnormal cases of COVID-19 by considering three different medical imaging modalities namely ultrasound imaging, X-ray images and CT scan images through introduced attention bottleneck residual network (AB-ResNet). It also aims to segment the abnormal infected area from normal images for localizing localising the disease infected area through the proposed edge based graph cut segmentation (E-GCS). Methodology AB-ResNet is used for classifying images whereas E-GCS segment the abnormal images. The study possess various advantages as it rely on DL and possess capability for accelerating the training speed of deep networks. It also enhance the network depth leading to minimum parameters, minimising the impact of vanishing gradient issue and attaining effective network performance with respect to better accuracy. Results/Conclusion Performance and comparative analysis is undertaken to evaluate the efficiency of the introduced system and results explores the efficiency of the proposed system in COVID-19 detection with high accuracy (99%).
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Affiliation(s)
- T Ahila
- Department of Computer Applications, Noorul Islam Centre For Higher Education, Kumaracoil, 629180, India
| | - A C Subhajini
- Department of Computer Applications, Noorul Islam Centre For Higher Education, Kumaracoil, 629180, India
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Ghose P, Uddin MA, Acharjee UK, Sharmin S. Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture. INTELLIGENT SYSTEMS WITH APPLICATIONS 2022; 16. [PMCID: PMC9536212 DOI: 10.1016/j.iswa.2022.200130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In recent years, coronavirus (Covid-19) has evolved into one of the world’s leading life-threatening severe viral illnesses. A self-executing accord system might be a better option to stop Covid-19 from spreading due to its quick diagnostic option. Many researches have already investigated various deep learning techniques, which have a significant impact on the quick and precise early detection of Covid-19. Most of the existing techniques, though, have not been trained and tested using a significant amount of data. In this paper, we purpose a deep learning technique enabled Convolutional Neural Network (CNN) to automatically diagnose Covid-19 from chest x-rays. To train and test our model, 10,293 x-rays, including 2875 x-rays of Covid-19, were collected as a data set. The applied dataset consists of three groups of chest x-rays: Covid-19, pneumonia, and normal patients. The proposed approach achieved 98.5% accuracy, 98.9% specificity, 99.2% sensitivity, 99.2% precision, and 98.3% F1-score. Distinguishing Covid-19 patients from pneumonia patients using chest x-ray, particularly for human eyes is crucial since both diseases have nearly identical characteristics. To address this issue, we have categorized Covid-19 and pneumonia using x-rays, achieving a 99.60% accuracy rate. Our findings show that the proposed model might aid clinicians and researchers in rapidly detecting Covid-19 patients, hence facilitating the treatment of Covid-19 patients.
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Affiliation(s)
- Partho Ghose
- Depaprtment of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh,Corresponding author
| | - Md. Ashraf Uddin
- Depaprtment of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Uzzal Kumar Acharjee
- Depaprtment of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Selina Sharmin
- Depaprtment of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
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Costa YMG, Silva SA, Teixeira LO, Pereira RM, Bertolini D, Britto AS, Oliveira LS, Cavalcanti GDC. COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers. SENSORS (BASEL, SWITZERLAND) 2022; 22:7303. [PMID: 36236402 PMCID: PMC9570662 DOI: 10.3390/s22197303] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.
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Affiliation(s)
- Yandre M. G. Costa
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Sergio A. Silva
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Lucas O. Teixeira
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | | | - Diego Bertolini
- Departamento Acadêmico de Ciência da Computação, Universidade Tecnológica Federal do Paraná, Campo Mourão 87301-899, Brazil
| | - Alceu S. Britto
- Departmento de Ciência da Computação, Pontifícia Universidade Católica do Paraná, Curitiba 80215-901, Brazil
| | - Luiz S. Oliveira
- Departamento de Informática, Universidade Federal do Paraná, Curitiba 81531-980, Brazil
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Ghose P, Alavi M, Tabassum M, Ashraf Uddin M, Biswas M, Mahbub K, Gaur L, Mallik S, Zhao Z. Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach. Front Genet 2022; 13:980338. [PMID: 36212141 PMCID: PMC9533058 DOI: 10.3389/fgene.2022.980338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/18/2022] [Indexed: 11/17/2022] Open
Abstract
COVID-19 has caused over 528 million infected cases and over 6.25 million deaths since its outbreak in 2019. The uncontrolled transmission of the SARS-CoV-2 virus has caused human suffering and the death of uncountable people. Despite the continuous effort by the researchers and laboratories, it has been difficult to develop reliable efficient and stable vaccines to fight against the rapidly evolving virus strains. Therefore, effectively preventing the transmission in the community and globally has remained an urgent task since its outbreak. To avoid the rapid spread of infection, we first need to identify the infected individuals and isolate them. Therefore, screening computed tomography (CT scan) and X-ray can better separate the COVID-19 infected patients from others. However, one of the main challenges is to accurately identify infection from a medical image. Even experienced radiologists often have failed to do it accurately. On the other hand, deep learning algorithms can tackle this task much easier, faster, and more accurately. In this research, we adopt the transfer learning method to identify the COVID-19 patients from normal individuals when there is an inadequacy of medical image data to save time by generating reliable results promptly. Furthermore, our model can perform both X-rays and CT scan. The experimental results found that the introduced model can achieve 99.59% accuracy for X-rays and 99.95% for CT scan images. In summary, the proposed method can effectively identify COVID-19 infected patients, could be a great way which will help to classify COVID-19 patients quickly and prevent the viral transmission in the community.
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Affiliation(s)
- Partho Ghose
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh
| | - Muhaddid Alavi
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Mehnaz Tabassum
- Center for Health Informatics, Macquarie University, Sydney, NSW, Australia
| | - Md. Ashraf Uddin
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Milon Biswas
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh
| | - Kawsher Mahbub
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh
| | - Loveleen Gaur
- Amity International Business School, Amity University, Noida, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, United States
| | - Zhongming Zhao
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, United States
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
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Multiclass Classification for Detection of COVID-19 Infection in Chest X-Rays Using CNN. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3289809. [PMID: 35965768 PMCID: PMC9372515 DOI: 10.1155/2022/3289809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/28/2022] [Accepted: 05/25/2022] [Indexed: 11/23/2022]
Abstract
Coronavirus took the world by surprise and caused a lot of trouble in all the important fields in life. The complexity of dealing with coronavirus lies in the fact that it is highly infectious and is a novel virus which is hard to detect with exact precision. The typical detection method for COVID-19 infection is the RT-PCR but it is a rather expensive method which is also invasive and has a high margin of error. Radiographies are a good alternative for COVID-19 detection given the experience of the radiologist and his learning capabilities. To make an accurate detection from chest X-Rays, deep learning technologies can be involved to analyze the radiographs, learn distinctive patterns of coronavirus' presence, find these patterns in the tested radiograph, and determine whether the sample is actually COVID-19 positive or negative. In this study, we propose a model based on deep learning technology using Convolutional Neural Networks and training it on a dataset containing a total of over 35,000 chest X-Ray images, nearly 16,000 for COVID-19 positive images, 15,000 for normal images, and 5,000 for pneumonia-positive images. The model's performance was assessed in terms of accuracy, precision, recall, and F1-score, and it achieved 99% accuracy, 0.98 precision, 1.02 recall, and 99.0% F1-score, thus outperforming other deep learning models from other studies.
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Laddha S, Mnasri S, Alghamdi M, Kumar V, Kaur M, Alrashidi M, Almuhaimeed A, Alshehri A, Alrowaily MA, Alkhazi I. COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study. Diagnostics (Basel) 2022; 12:1880. [PMID: 36010231 PMCID: PMC9406661 DOI: 10.3390/diagnostics12081880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/23/2022] [Accepted: 07/25/2022] [Indexed: 11/16/2022] Open
Abstract
In December 2019, the novel coronavirus disease 2019 (COVID-19) appeared. Being highly contagious and with no effective treatment available, the only solution was to detect and isolate infected patients to further break the chain of infection. The shortage of test kits and other drawbacks of lab tests motivated researchers to build an automated diagnosis system using chest X-rays and CT scanning. The reviewed works in this study use AI coupled with the radiological image processing of raw chest X-rays and CT images to train various CNN models. They use transfer learning and numerous types of binary and multi-class classifications. The models are trained and validated on several datasets, the attributes of which are also discussed. The obtained results of various algorithms are later compared using performance metrics such as accuracy, F1 score, and AUC. Major challenges faced in this research domain are the limited availability of COVID image data and the high accuracy of the prediction of the severity of patients using deep learning compared to well-known methods of COVID-19 detection such as PCR tests. These automated detection systems using CXR technology are reliable enough to help radiologists in the initial screening and in the immediate diagnosis of infected individuals. They are preferred because of their low cost, availability, and fast results.
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Affiliation(s)
- Saloni Laddha
- Computer Science and Engineering Department, National Institute of Technology Hamirpur, Hamirpur 177005, Himachal Pradesh, India; (S.L.); (V.K.)
| | - Sami Mnasri
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
- Department of Computer Science, ISSAT of Gafsa, University of Gafsa, Gafsa 2112, Tunisia
| | - Mansoor Alghamdi
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Vijay Kumar
- Computer Science and Engineering Department, National Institute of Technology Hamirpur, Hamirpur 177005, Himachal Pradesh, India; (S.L.); (V.K.)
| | - Manjit Kaur
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
| | - Malek Alrashidi
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Abdullah Almuhaimeed
- The National Centre for Genomics Technologies and Bioinformatics, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia
| | - Ali Alshehri
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Majed Abdullah Alrowaily
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Ibrahim Alkhazi
- College of Computers & Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia;
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Ravi V, Acharya V, Alazab M. A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images. CLUSTER COMPUTING 2022; 26:1181-1203. [PMID: 35874187 PMCID: PMC9295885 DOI: 10.1007/s10586-022-03664-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 05/21/2022] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
This paper proposes a multichannel deep learning approach for lung disease detection using chest X-rays. The multichannel models used in this work are EfficientNetB0, EfficientNetB1, and EfficientNetB2 pretrained models. The features from EfficientNet models are fused together. Next, the fused features are passed into more than one non-linear fully connected layer. Finally, the features passed into a stacked ensemble learning classifier for lung disease detection. The stacked ensemble learning classifier contains random forest and SVM in the first stage and logistic regression in the second stage for lung disease detection. The performance of the proposed method is studied in detail for more than one lung disease such as pneumonia, Tuberculosis (TB), and COVID-19. The performances of the proposed method for lung disease detection using chest X-rays compared with similar methods with the aim to show that the method is robust and has the capability to achieve better performances. In all the experiments on lung disease, the proposed method showed better performance and outperformed similar lung disease existing methods. This indicates that the proposed method is robust and generalizable on unseen chest X-rays data samples. To ensure that the features learnt by the proposed method is optimal, t-SNE feature visualization was shown on all three lung disease models. Overall, the proposed method has shown 98% detection accuracy for pediatric pneumonia lung disease, 99% detection accuracy for TB lung disease, and 98% detection accuracy for COVID-19 lung disease. The proposed method can be used as a tool for point-of-care diagnosis by healthcare radiologists.Journal instruction requires a city for affiliations; however, this is missing in affiliation 3. Please verify if the provided city is correct and amend if necessary.correct.
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Affiliation(s)
- Vinayakumar Ravi
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
| | - Vasundhara Acharya
- Manipal Institute of Technology (MIT), Manipal Academy of Higher Education (MAHE), Manipal, India
| | - Mamoun Alazab
- College of Engineering, IT and Environment, Charles Darwin University, Casuarina, NT Australia
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Roy PK, Kumar A. Early prediction of COVID-19 using ensemble of transfer learning. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 101:108018. [PMID: 35502295 PMCID: PMC9046104 DOI: 10.1016/j.compeleceng.2022.108018] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 04/12/2022] [Accepted: 04/15/2022] [Indexed: 06/14/2023]
Abstract
In the wake of the COVID-19 outbreak, automated disease detection has become a crucial part of medical science given the infectious nature of the coronavirus. This research aims to introduce a deep ensemble framework of transfer learning models for early prediction of COVID-19 from the respective chest X-ray images of the patients. The dataset used in this research was taken from the Kaggle repository having two classes-COVID-19 Positive and COVID-19 Negative. The proposed model achieved high accuracy on the test sample with minimum false positive prediction. It can assist doctors and technicians with early detection of COVID-19 infection. The patient's health can further be monitored remotely with the help of connected devices with the Internet, which may be termed as the Internet of Medical Things (IoMT). The proposed IoMT-based solution for the automatic detection of COVID-19 can be a significant step toward fighting the pandemic.
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Affiliation(s)
- Pradeep Kumar Roy
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Surat, Gujarat, India
| | - Abhinav Kumar
- Department of Computer Science and Engineering, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
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Kim T, Kim YG, Park S, Lee JK, Lee CH, Hyun SJ, Kim CH, Kim KJ, Chung CK. Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs. J Neurosurg Spine 2022; 37:104-111. [PMID: 35061993 DOI: 10.3171/2021.11.spine211136] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/11/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Magnetic resonance imaging (MRI) is the gold-standard tool for diagnosing lumbar spinal stenosis (LSS), but it is difficult to promptly examine all suspected cases with MRI considering the modality's high cost and limited accessibility. Although radiography is an efficient screening technique owing to its low cost, rapid operability, and wide availability, its diagnostic accuracy is relatively poor. In this study, the authors aimed to develop a deep learning model with a convolutional neural network (CNN) for diagnosing severe central LSS using radiography and to evaluate radiological diagnostic features using gradient-weighted class activation mapping (Grad-CAM). METHODS Patients who had undergone both spinal MRI and radiography in the period from May 1, 2005, to December 31, 2017, were screened. According to the formal MRI report, participants were consecutively included in the severe central LSS or healthy control group, and radiographs for both groups were collected. A CNN-based transfer learning algorithm was developed to classify radiographic findings as LSS or normal (binary classification). The proposed models were evaluated using six performance metrics: area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and positive and negative predictive values. RESULTS The VGG19 model achieved the highest accuracy with an AUROC of 90.0% (95% CI 89.8%-90.3%) by training 12,442 images. Accuracy was 82.8% (95% CI 82.5%-83.1%) by averaging 5-fold models. Feature points on Grad-CAM were reasonable, and the features could be categorized into reduced disc height, narrow foramina, short pedicle, and hyperdense facet joint. The AUROC in the extra validation was 89.3% (95% CI 88.7%-90.0%). Accuracy was 81.8% (95% CI 80.6%-83.0%) by averaging 5-fold models. Multivariate logistic regression analysis showed that a combination of demographic factors (age and sex) did not improve the model performance. CONCLUSIONS The algorithm trained by a CNN to identify central LSS on radiographs showed high diagnostic accuracy and is expected to be useful as a triage tool. The algorithm could accurately localize the stenotic lesion to assist physicians in the identification of LSS.
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Affiliation(s)
- Tackeun Kim
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
| | - Young-Gon Kim
- 2Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul
- 3AI Institute, Seoul National University, Seoul
| | - Seyeon Park
- 2Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul
| | - Jae-Koo Lee
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
| | - Chang-Hyun Lee
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
- 4Department of Neurosurgery, Seoul National University Hospital, Seoul
| | - Seung-Jae Hyun
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
- 5Seoul National University College of Medicine, Seoul; and
| | - Chi Heon Kim
- 4Department of Neurosurgery, Seoul National University Hospital, Seoul
- 5Seoul National University College of Medicine, Seoul; and
| | - Ki-Jeong Kim
- 1Department of Neurosurgery, Seoul National University Bundang Hospital, Seongnam
- 5Seoul National University College of Medicine, Seoul; and
| | - Chun Kee Chung
- 4Department of Neurosurgery, Seoul National University Hospital, Seoul
- 5Seoul National University College of Medicine, Seoul; and
- 6Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
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Ahmad M, Sadiq S, Eshmawi AA, Alluhaidan AS, Umer M, Ullah S, Nappi M. Industry 4.0 technologies and their applications in fighting COVID-19 pandemic using deep learning techniques. Comput Biol Med 2022; 145:105418. [PMID: 35334315 PMCID: PMC8935962 DOI: 10.1016/j.compbiomed.2022.105418] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 03/16/2022] [Accepted: 03/16/2022] [Indexed: 12/16/2022]
Abstract
The disease known as COVID-19 has turned into a pandemic and spread all over the world. The fourth industrial revolution known as Industry 4.0 includes digitization, the Internet of Things, and artificial intelligence. Industry 4.0 has the potential to fulfil customized requirements during the COVID-19 emergency crises. The development of a prediction framework can help health authorities to react appropriately and rapidly. Clinical imaging like X-rays and computed tomography (CT) can play a significant part in the early diagnosis of COVID-19 patients that will help with appropriate treatment. The X-ray images could help in developing an automated system for the rapid identification of COVID-19 patients. This study makes use of a deep convolutional neural network (CNN) to extract significant features and discriminate X-ray images of infected patients from non-infected ones. Multiple image processing techniques are used to extract a region of interest (ROI) from the entire X-ray image. The ImageDataGenerator class is used to overcome the small dataset size and generate ten thousand augmented images. The performance of the proposed approach has been compared with state-of-the-art VGG16, AlexNet, and InceptionV3 models. Results demonstrate that the proposed CNN model outperforms other baseline models with high accuracy values: 97.68% for two classes, 89.85% for three classes, and 84.76% for four classes. This system allows COVID-19 patients to be processed by an automated screening system with minimal human contact.
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Affiliation(s)
- Muhammad Ahmad
- Department of Information Security, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Saima Sadiq
- Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
| | - Ala' Abdulmajid Eshmawi
- Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
| | - Ala Saleh Alluhaidan
- Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Muhammad Umer
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
| | - Saleem Ullah
- Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
| | - Michele Nappi
- Department of Computer Science, University of Salerno, Fisciano, Italy.
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Alharbi AH, Hosni Mahmoud HA. Pneumonia Transfer Learning Deep Learning Model from Segmented X-rays. Healthcare (Basel) 2022; 10:987. [PMID: 35742039 PMCID: PMC9223174 DOI: 10.3390/healthcare10060987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/11/2022] [Accepted: 05/23/2022] [Indexed: 12/04/2022] Open
Abstract
Pneumonia is a common disease that occurs in many countries, more specifically, in poor countries. This disease is an obstructive pneumonia which has the same impression on pulmonary radiographs as other pulmonary diseases, which makes it hard to distinguish even for medical radiologists. Lately, image processing and deep learning models are established to rapidly and precisely diagnose pneumonia disease. In this research, we have predicted pneumonia diseases dependably from the X-ray images, employing image segmentation and machine learning models. A public labelled database is utilized with 4000 pneumonia disease X-rays and 4000 healthy X-rays. ImgNet and SqueezeNet are utilized for transfer learning from their previous computed weights. The proposed deep learning models are trained for classifying pneumonia and non-pneumonia cases. The following processes are presented in this paper: X-ray segmentation utilizing BoxENet architecture, X-ray classification utilizing the segmented chest images. We propose the improved BoxENet model by incorporating transfer learning from both ImgNet and SqueezeNet using a majority fusion model. Performance metrics such as accuracy, specificity, sensitivity and Dice are evaluated. The proposed Improved BoxENet model outperforms the other models in binary and multi-classification models. Additionally, the Improved BoxENet has higher speed compared to other models in both training and classification.
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Affiliation(s)
- Amal H. Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Hanan A. Hosni Mahmoud
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
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Sevli O. A deep learning-based approach for diagnosing COVID-19 on chest x-ray images, and a test study with clinical experts. Comput Intell 2022; 38:COIN12526. [PMID: 35941907 PMCID: PMC9348396 DOI: 10.1111/coin.12526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 03/06/2022] [Accepted: 04/05/2022] [Indexed: 01/20/2023]
Abstract
Pneumonia is among the common symptoms of the virus that causes COVID-19, which has turned into a worldwide pandemic. It is possible to diagnose pneumonia by examining chest radiographs. Chest x-ray (CXR) is a fast, low-cost, and practical method widely used in this field. The fact that different pathogens other than COVID-19 also cause pneumonia and the radiographic images of all are similar make it difficult to detect the source of the disease. In this study, automatic detection of COVID-19 cases over CXR images was tried to be performed using convolutional neural network (CNN), a deep learning technique. Classifications were carried out using six different architectures on the dataset consisting of 15,153 images of three different types: healthy, COVID-19, and other viral-induced pneumonia. In the classifications performed with five different state-of-art models, ResNet18, GoogLeNet, AlexNet, VGG16, and DenseNet161, and a minimal CNN architecture specific to this study, the most successful result was obtained with the ResNet18 architecture as 99.25% accuracy. Although the minimal CNN model developed for this study has a simpler structure, it was observed that it has a success to compete with more complex models. The performances of the models used in this study were compared with similar studies in the literature and it was revealed that they generally achieved higher success. The model with the highest success was transformed into a test application, tested by 10 volunteer clinicians, and it was concluded that it provides 99.06% accuracy in practical use. This result reveals that the conducted study can play the role of a successful decision support system for experts.
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Affiliation(s)
- Onur Sevli
- Faculty of Engineering and Architecture, Computer Engineering DepartmentBurdur Mehmet Akif Ersoy UniversityBurdurTurkey
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37
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Roland T, Böck C, Tschoellitsch T, Maletzky A, Hochreiter S, Meier J, Klambauer G. Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. J Med Syst 2022. [PMID: 35348909 DOI: 10.1101/2021.04.06.21254997] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
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Affiliation(s)
- Theresa Roland
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria.
| | - Carl Böck
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Thomas Tschoellitsch
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | | | - Sepp Hochreiter
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Jens Meier
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Günter Klambauer
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
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38
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Roland T, Böck C, Tschoellitsch T, Maletzky A, Hochreiter S, Meier J, Klambauer G. Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. J Med Syst 2022; 46:23. [PMID: 35348909 PMCID: PMC8960704 DOI: 10.1007/s10916-022-01807-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 02/10/2022] [Indexed: 12/23/2022]
Abstract
AbstractMany previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
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Affiliation(s)
- Theresa Roland
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria.
| | - Carl Böck
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Thomas Tschoellitsch
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | | | - Sepp Hochreiter
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Jens Meier
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Günter Klambauer
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
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39
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Shoaib MR, Emara HM, Elwekeil M, El-Shafai W, Taha TE, El-Fishawy AS, El-Rabaie ESM, El-Samie FEA. Hybrid classification structures for automatic COVID-19 detection. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 13:4477-4492. [PMID: 35280854 PMCID: PMC8898749 DOI: 10.1007/s12652-021-03686-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 12/21/2021] [Indexed: 06/14/2023]
Abstract
This paper explores the issue of COVID-19 detection from X-ray images. X-ray images, in general, suffer from low quality and low resolution. That is why the detection of different diseases from X-ray images requires sophisticated algorithms. First of all, machine learning (ML) is adopted on the features extracted manually from the X-ray images. Twelve classifiers are compared for this task. Simulation results reveal the superiority of Gaussian process (GP) and random forest (RF) classifiers. To extend the feasibility of this study, we have modified the feature extraction strategy to give deep features. Four pre-trained models, namely ResNet50, ResNet101, Inception-v3 and InceptionResnet-v2 are adopted in this study. Simulation results prove that InceptionResnet-v2 and ResNet101 with GP classifier achieve the best performance. Moreover, transfer learning (TL) is also introduced in this paper to enhance the COVID-19 detection process. The selected classification hierarchy is also compared with a convolutional neural network (CNN) model built from scratch to prove its quality of classification. Simulation results prove that deep features and TL methods provide the best performance that reached 100% for accuracy.
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Affiliation(s)
- Mohamed R. Shoaib
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952 Egypt
| | - Heba M. Emara
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952 Egypt
| | - Mohamed Elwekeil
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952 Egypt
- Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, Italy
| | - Walid El-Shafai
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952 Egypt
- Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, 11586 Saudi Arabia
| | - Taha E. Taha
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952 Egypt
| | - Adel S. El-Fishawy
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952 Egypt
| | - El-Sayed M. El-Rabaie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952 Egypt
| | - Fathi E. Abd El-Samie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952 Egypt
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
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40
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Alyasseri ZAA, Al‐Betar MA, Doush IA, Awadallah MA, Abasi AK, Makhadmeh SN, Alomari OA, Abdulkareem KH, Adam A, Damasevicius R, Mohammed MA, Zitar RA. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. EXPERT SYSTEMS 2022; 39:e12759. [PMID: 34511689 PMCID: PMC8420483 DOI: 10.1111/exsy.12759] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 05/17/2021] [Accepted: 06/07/2021] [Indexed: 05/02/2023]
Abstract
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
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Affiliation(s)
- Zaid Abdi Alkareem Alyasseri
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
- ECE Department‐Faculty of EngineeringUniversity of KufaNajafIraq
| | - Mohammed Azmi Al‐Betar
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Information TechnologyAl‐Huson University College, Al‐Balqa Applied UniversityIrbidJordan
| | - Iyad Abu Doush
- Computing Department, College of Engineering and Applied SciencesAmerican University of KuwaitSalmiyaKuwait
- Computer Science DepartmentYarmouk UniversityIrbidJordan
| | - Mohammed A. Awadallah
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Computer ScienceAl‐Aqsa UniversityGazaPalestine
| | - Ammar Kamal Abasi
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Faculty of Information TechnologyMiddle East UniversityAmmanJordan
| | | | | | - Afzan Adam
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
| | | | - Mazin Abed Mohammed
- College of Computer Science and Information TechnologyUniversity of AnbarAnbarIraq
| | - Raed Abu Zitar
- Sorbonne Center of Artificial IntelligenceSorbonne University‐Abu DhabiAbu DhabiUnited Arab Emirates
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Hu J, Han Z, Heidari AA, Shou Y, Ye H, Wang L, Huang X, Chen H, Chen Y, Wu P. Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine. Comput Biol Med 2022; 142:105166. [PMID: 35077935 PMCID: PMC8701842 DOI: 10.1016/j.compbiomed.2021.105166] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 12/20/2021] [Accepted: 12/20/2021] [Indexed: 01/08/2023]
Abstract
Coronavirus disease-2019 (COVID-19) has made the world more cautious about widespread viruses, and a tragic pandemic that was caused by a novel coronavirus has harmed human beings in recent years. The new coronavirus pneumonia outbreak is spreading rapidly worldwide. We collect arterial blood samples from 51 patients with a COVID-19 diagnosis. Blood gas analysis is performed using a Siemens RAPID Point 500 blood gas analyzer. To accurately determine the factors that play a decisive role in the early recognition and discrimination of COVID-19 severity, a prediction framework that is based on an improved binary Harris hawk optimization (HHO) algorithm in combination with a kernel extreme learning machine is proposed in this paper. This method uses specular reflection learning to improve the original HHO algorithm and is referred to as HHOSRL. The experimental results show that the selected indicators, such as age, partial pressure of oxygen, oxygen saturation, sodium ion concentration, and lactic acid, are essential for the early accurate assessment of COVID-19 severity by the proposed feature selection method. The simulation results show that the established methodlogy can achieve promising performance. We believe that our proposed model provides an effective strategy for accurate early assessment of COVID-19 and distinguishing disease severity. The codes of HHO will be updated in https://aliasgharheidari.com/HHO.html.
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Affiliation(s)
- Jiao Hu
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Zhengyuan Han
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, PR China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Yeqi Shou
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, PR China.
| | - Hua Ye
- Department of Pulmonary and Critical Care Medicine, Affiliated Yueqing Hospital, Wenzhou Medical University, Yueqing, 325600, China.
| | - Liangxing Wang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, PR China.
| | - Xiaoying Huang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, PR China.
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Yanfan Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, PR China.
| | - Peiliang Wu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, PR China.
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Romeih M, Mahrous MR, El Kassas M. Incidental radiological findings suggestive of COVID-19 in asymptomatic patients. World J Radiol 2022; 14:1-12. [PMID: 35126873 PMCID: PMC8788167 DOI: 10.4329/wjr.v14.i1.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 09/09/2021] [Accepted: 12/23/2021] [Indexed: 02/06/2023] Open
Abstract
Despite routine screening of patients for coronavirus disease 2019 (COVID-19) symptoms and signs at hospital entrances, patients may slip between the cracks and be incidentally discovered to have lung findings that could indicate COVID-19 infection on imaging obtained for other reasons. Multiple case reports and case series have been published to identify the pattern of this highly infectious disease. This article addresses the radiographic findings in different imaging modalities that may be incidentally seen in asymptomatic patients who carry COVID-19. In general, findings of COVID-19 infection may appear in computed tomography (CT), magnetic resonance imaging, positron emission tomography-CT, ultrasound, or plain X-rays that show lung or only apical or basal cuts. The identification of these characteristics by radiologists and clinicians is crucial because this would help in the early recognition of cases so that a rapid treatment protocol can be established, the immediate isolation to reduce community transmission, and the organization of close monitoring. Thus, it is important to both the patient and the physician that these findings are highlighted and reported.
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Affiliation(s)
- Marwa Romeih
- Department of Radiodiagnosis, Faculty of Medicine, Helwan University, Cairo 11795, Egypt
| | - Mary R Mahrous
- Department of Radiodiagnosis, National Heart institute, Cairo 11795, Egypt
| | - Mohamed El Kassas
- Department of Endemic Medicine, Faculty of Medicine, Helwan University, Cairo 11795, Egypt
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Imagawa K, Shiomoto K. Performance change with the number of training data: A case study on the binary classification of COVID-19 chest X-ray by using convolutional neural networks. Comput Biol Med 2022; 142:105251. [PMID: 35093727 DOI: 10.1016/j.compbiomed.2022.105251] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/15/2022] [Accepted: 01/19/2022] [Indexed: 12/24/2022]
Abstract
One of the features of artificial intelligence/machine learning-based medical devices resides in their ability to learn from real-world data. However, obtaining a large number of training data in the early phase is difficult, and the device performance may change after their first introduction into the market. To introduce the safety and effectiveness of these devices into the market in a timely manner, an appropriate post-market performance change plan must be established at the timing of the premarket approval. In this work, we evaluate the performance change with the variation of the number of training data. Two publicly available datasets are used: one consisting of 4000 images for COVID-19 and another comprising 4000 images for Normal. The dataset was split into 7000 images for training and validation, also 1000 images for test. Furthermore, the training and validation data were selected as different 16 datasets. Two different convolutional neural networks, namely AlexNet and ResNet34, with and without a fine-tuning method were used to classify two image types. The area under the curve, sensitivity, and specificity were evaluated for each dataset. Our result shows that all performances were rapidly improved as the number of training data was increased and reached an equilibrium state. AlexNet outperformed ResNet34 when the number of images was small. The difference tended to decrease as the number of training data increased, and the fine-tuning method improved all performances. In conclusion, the appropriate model and method should be selected considering the intended performance and available number of data.
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Affiliation(s)
- Kuniki Imagawa
- Tokyo City University, Faculty of Information Technology, 1-28-1 Tamazutsumi, Setagaya-ku, Tokyo, 158-8557, Japan.
| | - Kohei Shiomoto
- Tokyo City University, Faculty of Information Technology, 1-28-1 Tamazutsumi, Setagaya-ku, Tokyo, 158-8557, Japan
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Di Basilio F, Esposisto G, Monoscalco L, Giansanti D. The Artificial Intelligence in Digital Radiology: Part 2: Towards an Investigation of acceptance and consensus on the Insiders. Healthcare (Basel) 2022; 10:153. [PMID: 35052316 PMCID: PMC8775988 DOI: 10.3390/healthcare10010153] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/19/2021] [Accepted: 01/10/2022] [Indexed: 02/04/2023] Open
Abstract
Background. The study deals with the introduction of the artificial intelligence in digital radiology. There is a growing interest in this area of scientific research in acceptance and consensus studies involving both insiders and the public, based on surveys focused mainly on single professionals. Purpose. The goal of the study is to perform a contemporary investigation on the acceptance and the consensus of the three key professional figures approaching in this field of application: (1) Medical specialists in image diagnostics: the medical specialists (MS)s; (2) experts in physical imaging processes: the medical physicists (MP)s; (3) AI designers: specialists of applied sciences (SAS)s. Methods. Participants (MSs = 92: 48 males/44 females, averaged age 37.9; MPs = 91: 43 males/48 females, averaged age 36.1; SAS = 90: 47 males/43 females, averaged age 37.3) were properly recruited based on specific training. An electronic survey was designed and submitted to the participants with a wide range questions starting from the training and background up to the different applications of the AI and the environment of application. Results. The results show that generally, the three professionals show (a) a high degree of encouraging agreement on the introduction of AI both in imaging and in non-imaging applications using both standalone applications and/or mHealth/eHealth, and (b) a different consent on AI use depending on the training background. Conclusions. The study highlights the usefulness of focusing on both the three key professionals and the usefulness of the investigation schemes facing a wide range of issues. The study also suggests the importance of different methods of administration to improve the adhesion and the need to continue these investigations both with federated and specific initiatives.
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Affiliation(s)
- Francesco Di Basilio
- Facoltà di Medicina e Psicologia, Sapienza University, Piazzale Aldo Moro, 00185 Rome, Italy; (F.D.B.); (G.E.)
| | - Gianluca Esposisto
- Facoltà di Medicina e Psicologia, Sapienza University, Piazzale Aldo Moro, 00185 Rome, Italy; (F.D.B.); (G.E.)
| | - Lisa Monoscalco
- Faculty of Engineering, Tor Vergata University, 00133 Rome, Italy;
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45
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Bayram F, Eleyan A. COVID-19 detection on chest radiographs using feature fusion based deep learning. SIGNAL, IMAGE AND VIDEO PROCESSING 2022; 16:1455-1462. [PMID: 35096182 PMCID: PMC8784235 DOI: 10.1007/s11760-021-02098-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 08/10/2021] [Accepted: 11/17/2021] [Indexed: 05/17/2023]
Abstract
The year 2020 will certainly be remembered in human history as the year in which humans faced a global pandemic that drastically affected every living soul on planet earth. The COVID-19 pandemic certainly had a massive impact on human's social and daily lives. The economy and relations of all countries were also radically impacted. Due to such unexpected situations, healthcare systems either collapsed or failed under colossal pressure to cope with the overwhelming numbers of patients arriving at emergency rooms and intensive care units. The COVID -19 tests used for diagnosis were expensive, slow, and gave indecisive results. Unfortunately, such a hindered diagnosis of the infection prevented abrupt isolation of the infected people which, in turn, caused the rapid spread of the virus. In this paper, we proposed the use of cost-effective X-ray images in diagnosing COVID-19 patients. Compared to other imaging modalities, X-ray imaging is available in most healthcare units. Deep learning was used for feature extraction and classification by implementing a multi-stream convolutional neural network model. The model extracts and concatenates features from its three inputs, namely; grayscale, local binary patterns, and histograms of oriented gradients images. Extensive experiments using fivefold cross-validation were carried out on a publicly available X-ray database with 3886 images of three classes. Obtained results outperform the results of other algorithms with an accuracy of 97.76%. The results also show that the proposed model can make a significant contribution to the rapidly increasing workload in health systems with an artificial intelligence-based automatic diagnosis tool.
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Affiliation(s)
- Fatih Bayram
- Mechatronics Engineering Department, Faculty of Technology, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Alaa Eleyan
- College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait
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46
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Banerjee A, Bhattacharya R, Bhateja V, Singh PK, Lay-Ekuakille A, Sarkar R. COFE-Net: An ensemble strategy for Computer-Aided Detection for COVID-19. MEASUREMENT : JOURNAL OF THE INTERNATIONAL MEASUREMENT CONFEDERATION 2022; 187:110289. [PMID: 34663998 PMCID: PMC8516129 DOI: 10.1016/j.measurement.2021.110289] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/29/2021] [Accepted: 10/03/2021] [Indexed: 05/26/2023]
Abstract
Biomedical images contain a large volume of sensor measurements, which can reveal the descriptors of the disease under investigation. Computer-based analysis of such measurements helps detect the disease, and thereby swiftly aid medical professionals to choose adequate therapy. In this paper, we propose a robust deep learning ensemble framework known as COVID Fuzzy Ensemble Network, or COFE-Net. This strategy is proposed for the task of COVID-19 screening from chest X-rays (CXR) and CT Scans, as a part of Computer-Aided Detection (CADe) for medical practitioners. We leverage the strategy of Transfer Learning for Convolutional Neural Networks (CNNs) widely adopted in recent literature, and further propose an efficient ensemble network for their combination. The principles of fuzzy logic have been leveraged to combine the measured decision scores generated by three state-of-the-art CNNs - Inception V3, Inception ResNet V2 and DenseNet 201 - through the Choquet fuzzy integral. Experimental results support the efficacy of our approach over empirical ensembling, as the fuzzy ensembling strategy for biomedical measurement consists of dynamic refactoring of the classifier ensemble weights on the fly, based upon the confidence scores for coalitions of inputs. This is the chief advantage of our biomedical measurement strategy over others as other methods do not adjust to the multiple generated measurements dynamically unlike ours.Impressive results on multiple datasets demonstrate the effectiveness of the proposed method. The source code of our proposed method is made available at: https://github.com/theavicaster/covid-cade-ensemble.
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Affiliation(s)
- Avinandan Banerjee
- Department of Information Technology, Jadavpur University, Kolkata 700106, India
| | - Rajdeep Bhattacharya
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
| | - Vikrant Bhateja
- Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow 226028, Uttar Pradesh, India
- Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India
| | - Pawan Kumar Singh
- Department of Information Technology, Jadavpur University, Kolkata 700106, India
| | - Aime' Lay-Ekuakille
- Dipartimento d'Ingegneria dell'Innovazione (DII), Università del Salento (Dept of Innovation Engineering, University of Salento) Via Monteroni, Ed. "Corpo O" 73100 Lecce (IT), Italy
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
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Bhattacharyya A, Bhaik D, Kumar S, Thakur P, Sharma R, Pachori RB. A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images. Biomed Signal Process Control 2022; 71:103182. [PMID: 34580596 PMCID: PMC8457928 DOI: 10.1016/j.bspc.2021.103182] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/22/2021] [Accepted: 09/20/2021] [Indexed: 12/13/2022]
Abstract
In this global pandemic situation of coronavirus disease (COVID-19), it is of foremost priority to look up efficient and faster diagnosis methods for reducing the transmission rate of the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recent research has indicated that radio-logical images carry essential information about the COVID-19 virus. Therefore, artificial intelligence (AI) assisted automated detection of lung infections may serve as a potential diagnostic tool. It can be augmented with conventional medical tests for tackling COVID-19. In this paper, we propose a new method for detecting COVID-19 and pneumonia using chest X-ray images. The proposed method can be described as a three-step process. The first step includes the segmentation of the raw X-ray images using the conditional generative adversarial network (C-GAN) for obtaining the lung images. In the second step, we feed the segmented lung images into a novel pipeline combining key points extraction methods and trained deep neural networks (DNN) for extraction of discriminatory features. Several machine learning (ML) models are employed to classify COVID-19, pneumonia, and normal lung images in the final step. A comparative analysis of the classification performance is carried out among the different proposed architectures combining DNNs, key point extraction methods, and ML models. We have achieved the highest testing classification accuracy of 96.6% using the VGG-19 model associated with the binary robust invariant scalable key-points (BRISK) algorithm. The proposed method can be efficiently used for screening of COVID-19 infected patients.
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Affiliation(s)
- Abhijit Bhattacharyya
- Department of Electronics and Communication Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, India
| | - Divyanshu Bhaik
- Department of Electronics and Communication Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, India
| | - Sunil Kumar
- Department of Electronics and Communication Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, India
| | - Prayas Thakur
- Department of Electronics and Communication Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, India
| | - Rahul Sharma
- Department of Electronics and Communication Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, India
| | - Ram Bilas Pachori
- Department of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
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Appasami G, Nickolas S. A deep learning-based COVID-19 classification from chest X-ray image: case study. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2022; 231:3767-3777. [PMID: 35996535 PMCID: PMC9386662 DOI: 10.1140/epjs/s11734-022-00647-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 07/26/2022] [Indexed: 05/02/2023]
Abstract
The novel corona virus disease (COVID-19) is a pandemic disease that is currently affecting over 200 countries around the world and more than 6 millions of people died in last 2 years. Early detection of COVID-19 can mitigate and control its spread. Reverse transcription polymerase chain reaction (RT-CPR), Chest X-ray (CXR) scan, and Computerized Tomography (CT) scan are used to identify the COVID-19. Chest X-ray image analysis is relatively time efficient than compared with RT-CPR and CT scan. Its cost-effectiveness make it a good choice for COVID-19 Classification. We propose a deep learning based Convolutional Neural Network model for detection of COVID-19 from CXR. Chest X-ray images are collected from various sources dataset for training with augmentation and evaluating our model, which is widely used for COVID-19 detection and diagnosis. A Deep Convolutional neural network (CNN) based model for analysis of COVID-19 with data augmentation is proposed, which uses the patient's chest X-ray images for the diagnosis of COVID-19 with an aim to help the physicians to assist the diagnostic process among high workload conditions. The overall accuracy of 93 percent for COVID-19 Classification is achieved by choosing best optimizer.
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Affiliation(s)
- G. Appasami
- National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamilnadu India
| | - S. Nickolas
- National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamilnadu India
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Sarker S, Jamal L, Ahmed SF, Irtisam N. Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. ROBOTICS AND AUTONOMOUS SYSTEMS 2021; 146:103902. [PMID: 34629751 PMCID: PMC8493645 DOI: 10.1016/j.robot.2021.103902] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/03/2021] [Accepted: 09/13/2021] [Indexed: 05/05/2023]
Abstract
The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.
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Affiliation(s)
- Sujan Sarker
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Lafifa Jamal
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Syeda Faiza Ahmed
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Niloy Irtisam
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
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50
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COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network. Health Inf Sci Syst 2021; 9:36. [PMID: 34659742 PMCID: PMC8509906 DOI: 10.1007/s13755-021-00166-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/20/2021] [Indexed: 12/19/2022] Open
Abstract
Purpose Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. Methods We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model’s performance. From the external test dataset, we calculated the model’s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. Conclusions Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s13755-021-00166-4.
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