Computer-Aided Detection of Skin Cancer Detection from Lesion Images Via Deep Learning Techniques: A Review

Abstract

Abstract: Skin cancer is a widespread disease in many countries, with melanoma being the leading cause of skin cancer-related deaths worldwide. To detect skin cancers, including malignant melanoma and other types, deep learning-based algorithms have been developed for image classification. Early recognition of skin cancer signs is essential due to the increasing incidence of the disease, its high fatality rate, and expensive medical treatments. This study presents a literature review of various techniques used to identify skin cancer. These systems utilize image processing techniques such as feature extraction, segmentation, and classification to distinguish melanoma and other skin conditions. The article also provides information on skin cancer types, stages, and treatments, and highlights the various deep learning techniques employed in diagnosis. Although researchers have developed advanced machine learning methods to detect each stage of melanoma cancer, there is still a need for more accurate, faster, and affordable detection methods. The article emphasizes the importance of continued research in this field to address future directions for skin cancer detection.