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Cited by in F6Publishing
For: Banerjee S, Singh SK, Chakraborty A, Das A, Bag R. Melanoma Diagnosis Using Deep Learning and Fuzzy Logic. Diagnostics (Basel) 2020;10:E577. [PMID: 32784837 DOI: 10.3390/diagnostics10080577] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
Number Citing Articles
1 Rout R, Parida P, Alotaibi Y, Alghamdi S, Khalaf OI. Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering. Symmetry 2021;13:2085. [DOI: 10.3390/sym13112085] [Cited by in Crossref: 15] [Cited by in F6Publishing: 7] [Article Influence: 15.0] [Reference Citation Analysis]
2 Nawaz M, Nazir T, Masood M, Ali F, Khan MA, Tariq U, Sahar N, Damaševičius R. Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network. Int J Imaging Syst Tech. [DOI: 10.1002/ima.22750] [Reference Citation Analysis]
3 Martin-Gonzalez M, Azcarraga C, Martin-Gil A, Carpena-Torres C, Jaen P. Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population. Int J Environ Res Public Health 2022;19:3892. [PMID: 35409575 DOI: 10.3390/ijerph19073892] [Reference Citation Analysis]
4 Stadelmann SA, Blüthgen C, Milanese G, Nguyen-Kim TDL, Maul JT, Dummer R, Frauenfelder T, Eberhard M. Lung Nodules in Melanoma Patients: Morphologic Criteria to Differentiate Non-Metastatic and Metastatic Lesions. Diagnostics (Basel) 2021;11:837. [PMID: 34066913 DOI: 10.3390/diagnostics11050837] [Reference Citation Analysis]
5 Yu C, Helwig EJ. The role of AI technology in prediction, diagnosis and treatment of colorectal cancer. Artif Intell Rev 2021;:1-21. [PMID: 34248245 DOI: 10.1007/s10462-021-10034-y] [Reference Citation Analysis]
6 Silva LA, Sanchez San Blas H, Peral García D, Sales Mendes A, Villarubia González G. An Architectural Multi-Agent System for a Pavement Monitoring System with Pothole Recognition in UAV Images. Sensors (Basel) 2020;20:E6205. [PMID: 33143311 DOI: 10.3390/s20216205] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
7 Khouloud S, Ahlem M, Fadel T, Amel S. W-net and inception residual network for skin lesion segmentation and classification. Appl Intell. [DOI: 10.1007/s10489-021-02652-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
8 Shorfuzzaman M. An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection. Multimedia Systems. [DOI: 10.1007/s00530-021-00787-5] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Popescu D, El-Khatib M, El-Khatib H, Ichim L. New Trends in Melanoma Detection Using Neural Networks: A Systematic Review. Sensors (Basel) 2022;22:496. [PMID: 35062458 DOI: 10.3390/s22020496] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
10 Nawaz M, Mehmood Z, Nazir T, Naqvi RA, Rehman A, Iqbal M, Saba T. Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering. Microsc Res Tech 2021. [PMID: 34448519 DOI: 10.1002/jemt.23908] [Reference Citation Analysis]
11 Mohapatra S, Satpathy S, Mohanty SN. A comparative knowledge base development for cancerous cell detection based on deep learning and fuzzy computer vision approach. Multimed Tools Appl 2022;81:24799-814. [DOI: 10.1007/s11042-022-12824-0] [Reference Citation Analysis]
12 Ningrum DNA, Yuan SP, Kung WM, Wu CC, Tzeng IS, Huang CY, Li JY, Wang YC. Deep Learning Classifier with Patient's Metadata of Dermoscopic Images in Malignant Melanoma Detection. J Multidiscip Healthc 2021;14:877-85. [PMID: 33907414 DOI: 10.2147/JMDH.S306284] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
13 Talpur N, Abdulkadir SJ, Alhussian H, Hasan MH, Aziz N, Bamhdi A. Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey. Artif Intell Rev. [DOI: 10.1007/s10462-022-10188-3] [Reference Citation Analysis]