  Skin cancer is the most common type of cancer. Specifically, melanoma is the
cause of 75% of skin cancer deaths, although it is the least common skin
cancer. Better detection of melanoma could have a positive impact on millions
of people. The ISIC archive contains the largest publicly available collection
of dermatoscopic images of skin lesions. In this research, we investigate the
efficacy of applying advanced deep learning techniques in computer vision to
identify melanoma in images of skin lesions. Through reviewing previous
methods, including pre-trained models, deep-learning classifiers, transfer
learning, etc., we demonstrate the applicability of the popular deep learning
methods on critical clinical problems such as identifying melanoma. Finally, we
proposed a processing flow with a validation AUC greater than 94% and a
sensitivity greater than 90% on ISIC 2016 - 2020 datasets.
