Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals and medical instruments are significant issues in this field. In this context, over the past few years, deep learning models applied to automated skin cancer detection have become a trend. In this paper, we present an overview of the recent advances reported in this field as well as a discussion about the challenges and opportunities for improvement in the current models. In addition, we also present some important aspects regarding the use of these models in smartphones and indicate future directions we believe the field will take.
@article{arxiv.1912.03280,
title = {Recent advances in deep learning applied to skin cancer detection},
author = {Andre G. C. Pacheco and Renato A. Krohling},
journal= {arXiv preprint arXiv:1912.03280},
year = {2019}
}
Comments
Paper accepted in the Retrospectives Workshop @ NeurIPS 2019