A Survey on Deep Learning Techniques for Skin Lesion Classification

Abstract

Skin problems are common in day-to-day living. Skin lesions cause patients to be emotionally and psychologically burdened, which may be worse than the physical disorders. Skin lesions must be detected early to receive effective treatment. The diagnostic method must be precise and performed within a reasonable time. Many skin lesions look similar, which increases the potential of human error when classifying them. Deep learning's use in disease diagnosis has been a key focus of dermatological research. Skin lesion classification based on deep learning aids in the automatic skin lesion classification by dermoscopy, removing errors caused by naked eye inspection. The goal of this paper is to provide a quick survey on deep learning based skin lesions categorization, and outline the features of skin lesions. The essential stages of skin lesions and elements that affect dermatological diagnosis are reviewed, and the current difficulties and prospects of classification are discussed. The findings suggest that a deep learning-based skin lesion detection technique may beat expert dermatologists in some scenarios, and that there is a lot of room for further study.