1 |
Farnoosh R, Noushkaran H. Application of a Modified Combinational Approach to Brain Tumor Detection in MR Images. J Digit Imaging 2022. [PMID: 35641677 DOI: 10.1007/s10278-022-00653-4] [Reference Citation Analysis]
|
2 |
Zhu Z, Lu S, Wang S, Gorriz JM, Zhang Y. DSNN: A DenseNet-Based SNN for Explainable Brain Disease Classification. Front Syst Neurosci 2022;16:838822. [DOI: 10.3389/fnsys.2022.838822] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
|
3 |
Das S, Nayak G, Saba L, Kalra M, Suri JS, Saxena S. An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105273] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 8.0] [Reference Citation Analysis]
|
4 |
Ilhan A, Sekeroglu B, Abiyev R. Brain tumor segmentation in MRI images using nonparametric localization and enhancement methods with U-net. Int J CARS. [DOI: 10.1007/s11548-022-02566-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
|
5 |
Bhima K, Neelakantappa M, Dasaradh Ramaiah K, Jagan A. Contemporary Technique for Detection of Brain Tumor in Fluid-Attenuated Inversion Recovery Magnetic Resonance Imaging (MRI) Images. Smart Intelligent Computing and Applications, Volume 2 2022. [DOI: 10.1007/978-981-16-9705-0_12] [Reference Citation Analysis]
|
6 |
Kalaiselvi T, Anitha T, Sriramakrishnan P. Data preprocessing techniques for MRI brain scans using deep learning models. Brain Tumor MRI Image Segmentation Using Deep Learning Techniques 2022. [DOI: 10.1016/b978-0-323-91171-9.00006-5] [Reference Citation Analysis]
|
7 |
Lu SY, Satapathy SC, Wang SH, Zhang YD. PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors. Front Cell Dev Biol 2021;9:765654. [PMID: 34722549 DOI: 10.3389/fcell.2021.765654] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
|
8 |
Thiruvenkadam K, Nagarajan K. Fully automatic brain tumor extraction and tissue segmentation from multimodal MRI brain images. Int J Imaging Syst Technol 2021;31:336-350. [DOI: 10.1002/ima.22459] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
|
9 |
Győrfi Á, Szilágyi L, Kovács L. A Fully Automatic Procedure for Brain Tumor Segmentation from Multi-Spectral MRI Records Using Ensemble Learning and Atlas-Based Data Enhancement. Applied Sciences 2021;11:564. [DOI: 10.3390/app11020564] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
|
10 |
Bhima K, Jagan A. Development of Robust Framework for Automatic Segmentation of Brain MRI Images. Smart Computing Techniques and Applications 2021. [DOI: 10.1007/978-981-16-0878-0_51] [Reference Citation Analysis]
|
11 |
Fulop T, Gyorfi A, Csaholczi S, Kovacs L, Szilagyi L. Brain Tumor Segmentation from Multi-Spectral MRI Data Using Cascaded Ensemble Learning. 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE) 2020. [DOI: 10.1109/sose50414.2020.9130550] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
|
12 |
Valsalan P, Sriramakrishnan P, Sridhar S, Latha GCP, Priya A, Ramkumar S, Singh AR, Rajendran T. Knowledge based fuzzy c-means method for rapid brain tissues segmentation of magnetic resonance imaging scans with CUDA enabled GPU machine. J Ambient Intell Human Comput. [DOI: 10.1007/s12652-020-02132-6] [Cited by in Crossref: 21] [Cited by in F6Publishing: 10] [Article Influence: 7.0] [Reference Citation Analysis]
|