Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation skin lesions necessary to assess characteristics disease; however, it limited by long timelines and variety in interpretation. As early accurate diagnosis SC crucial increase patient survival rates, machine-learning (ML) deep-learning (DL) approaches have been developed overcome these issues support dermatologists. We present a systematic literature review recent research on use machine learning classify with aim providing solid starting point for researchers beginning work this area. A search was conducted several electronic databases applying inclusion/exclusion filters review, only those documents that clearly completely described procedures performed reported results obtained were selected. Sixty-eight articles selected, which majority DL approaches, particular convolutional neural networks (CNN), while smaller portion rely ML techniques or hybrid ML/DL detection classification. Many methods show high performance as classifiers lesions. The promising date bode well not-too-distant inclusion clinical practice.