A survey on computer vision approaches for automated classification of skin diseases
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-19301-w.pdf
Reference141 articles.
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2. Seth D, Cheldize K, Brown D, Freeman EE (2017) Global burden of skin disease: inequities and innovations. Current dermatology reports 6:204–210
3. Yakupu A, Aimaier R, Yuan B, Chen B, Cheng J, Zhao Y, Peng Y, Dong J, Lu S (2023) The burden of skin and subcutaneous diseases: findings from the global burden of disease study 2019. Front Public Health 11:1145513
4. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118
5. Pham T-C, Luong C-M, Hoang V-D, Doucet A (2021) Ai outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-cnn architecture with custom mini-batch logic and loss function. Sci Rep 11(1):17485
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