Skin lesion segmentation using deep learning for images acquired from smartphones

Skin lesion is an abnormal growth of skin cells that may become a skin cancer, which is a major health issue around the world and its incidence has been increasing throughout the years. Early detection is critical to increase the survival probability and, for economically emerging countries, it is a real problem, since there is a lack of specialists and medical tools. Smartphones are a low-cost device that may help to tackle this problem. However, acquiring a satisfactory amount of images taken using this tool is a hard task. In this sense, in partnership with a group of dermatologists, we developed an application to collect skin lesion images using smartphones' camera and create a new clinical dataset. In this work, we present a methodology using deep learning, color space combination and conditional random fields to segment the created dataset. We present a carefully investigation regarding the color spaces and the post-processing that allow us to raise some important remarks about the skin lesion ground truth images that strongly affect the final segmentation.