Deep Learning Models for Skin Cancer Detection: A review

Skin cancer is one of those most common cancers that affect people. Due to various factors, DNA mutations grow out of control and eventually lead to skin cancer. Early cancer detection can improve the likelihood of successful treatment. Applications for computer-aided diagnosis are now used in almost every industry, especially the health sector. The data of ill people is saved in computers to create biomedical datasets. It is crucial to find a reliable method for early skin cancer diagnosis and type classification. Computer- aided detection using Deep Learning (DL) and Machine Learning (ML) shows tremendous growth in the medical field. Medical images are considered as the actual origin of appropriate information required for diagnosis of disease. This paper presents a thorough and methodical analysis of deep learning methods for skin cancer early detection. On this subject, a great deal of research has been done. Thus, it's imperative to assess, classify, and integrate the results of previous research. In order to facilitate comprehension of research findings, they are presented using tables, methodologies, and frameworks.