Automatic Segmentation of Melanoma Skin Cancer Using Deep Learning

Segmentation is a crucial step to obtain success for classifying medical images. However, it is a highly complex task due to the abnormal shapes and the presence of other artifacts. In this study, a melanoma segmentation approach based on deep learning is proposed. In conjunction with post-processing techniques, the proposed modified U-net network has proven to be highly effective in lesions segmentation. The experiments were performed in two public datasets (PH2 and DermIS) and reached an average Dice coefficient of 0.933 in the PH2 dataset and Dice = 0.872 in the DermIS dataset. Considering the high-performance methodologies available in the literature, the proposed solution is very promising, surpassing other methods with very promising results.