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Cited by in F6Publishing
For: Pacheco AGC, Lima GR, Salomão AS, Krohling B, Biral IP, de Angelo GG, Alves FCR Jr, Esgario JGM, Simora AC, Castro PBC, Rodrigues FB, Frasson PHL, Krohling RA, Knidel H, Santos MCS, do Espírito Santo RB, Macedo TLSG, Canuto TRP, de Barros LFS. PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones. Data Brief 2020;32:106221. [PMID: 32939378 DOI: 10.1016/j.dib.2020.106221] [Cited by in Crossref: 9] [Cited by in F6Publishing: 3] [Article Influence: 4.5] [Reference Citation Analysis]
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1 Medhat S, Abdel-galil H, Aboutabl AE, Saleh H. Skin cancer diagnosis using convolutional neural networks for smartphone images: A comparative study. Journal of Radiation Research and Applied Sciences 2022;15:262-7. [DOI: 10.1016/j.jrras.2022.03.008] [Reference Citation Analysis]
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4 Farajnia S. Overcoming the Data Gap for the Remote Diagnosis of Skin Cancer. Patterns (N Y) 2020;1:100117. [PMID: 33205141 DOI: 10.1016/j.patter.2020.100117] [Reference Citation Analysis]
5 Moldovanu S, Obreja CD, Biswas KC, Moraru L. Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks. Diagnostics (Basel) 2021;11:936. [PMID: 34067493 DOI: 10.3390/diagnostics11060936] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
6 Dimililer K, Sekeroglu B. Skin Lesion Classification Using CNN-based Transfer Learning Model. GAZI UNIVERSITY JOURNAL OF SCIENCE. [DOI: 10.35378/gujs.1063289] [Reference Citation Analysis]
7 Dascalu A, Walker BN, Oron Y, David EO. Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms. J Cancer Res Clin Oncol 2021. [PMID: 34546412 DOI: 10.1007/s00432-021-03809-x] [Reference Citation Analysis]