View article

Skin cancer, the most common human malignancy 1, 2, 3, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) 4, 5 show potential for general and highly variable tasks across many fine-grained object categories 6, 7, 8, 9, 10, 11. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets 12—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on …