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Skin cancer is the most common type of cancer. Specifically, melanoma is thecause of 75% of skin cancer deaths, although it is the least common skincancer. Better detection of melanoma could have a positive impact on millionsof people. The ISIC archive contains the largest publicly available collectionof dermatoscopic images of skin lesions. In this research, we investigate theefficacy of applying advanced deep learning techniques in computer vision toidentify melanoma in images of skin lesions. Through reviewing previousmethods, including pre-trained models, deep-learning classifiers, transferlearning, etc., we demonstrate the applicability of the popular deep learningmethods on critical clinical problems such as identifying melanoma. Finally, weproposed a processing flow with a validation AUC greater than 94% and asensitivity greater than 90% on ISIC 2016 - 2020 datasets.

A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets