Automated Diagnosis of Skin Lesions

Recent years have seen significant progress in the automatic diagnosis of pigmented skin lesions, including advances in self-surveillance technologies accessible to patients and computer-aided diagnosis (CAD) tools for dermatologists. Rapid advances in mobile technologies and applications are playing a central role in providing educational aids and self-surveillance tools for patient use. At the same time, machine learning, specifically, deep learning is a fast-growing field that is being used for multiple medical imaging related problems, such as skin lesions classification. Recent studies based on deep networks produced promising results which have the potential to change the landscape of skin lesion diagnosis. Systems created based on these new advancements aim to provide support for both dermatologists in the decision making process and for patients that do not have access to skin professionals. This paper focuses on the current state of automated skin lesion diagnosis, while also providing a comprehensive view into the challenges and opportunities in dermatology care.