  Melanoma is the deadliest form of skin cancer. While curable with early
detection, only highly trained specialists are capable of accurately
recognizing the disease. As expertise is in limited supply, automated systems
capable of identifying disease could save lives, reduce unnecessary biopsies,
and reduce costs. Toward this goal, we propose a system that combines recent
developments in deep learning with established machine learning approaches,
creating ensembles of methods that are capable of segmenting skin lesions, as
well as analyzing the detected area and surrounding tissue for melanoma
detection. The system is evaluated using the largest publicly available
benchmark dataset of dermoscopic images, containing 900 training and 379
testing images. New state-of-the-art performance levels are demonstrated,
leading to an improvement in the area under receiver operating characteristic
curve of 7.5% (0.843 vs. 0.783), in average precision of 4% (0.649 vs. 0.624),
and in specificity measured at the clinically relevant 95% sensitivity
operating point 2.9 times higher than the previous state-of-the-art (36.8%
specificity compared to 12.5%). Compared to the average of 8 expert
dermatologists on a subset of 100 test images, the proposed system produces a
higher accuracy (76% vs. 70.5%), and specificity (62% vs. 59%) evaluated at an
equivalent sensitivity (82%).
