Abstract
Skin cancer is one of the most prevalent forms of cancer globally, with rising incidence rates posing significant challenges to healthcare systems. Early detection and accurate diagnosis are critical for effective treatment and patient outcomes. In recent years, machine learning (ML) algorithms have emerged as powerful tools for analyzing medical imaging data and assisting clinicians in diagnosing skin cancer. This review paper provides a comprehensive overview of the ML classification algorithms in the context of skin cancer detection and diagnosis. We discuss various types of skin cancer, including melanoma, basal cell carcinoma, and squamous cell carcinoma, along with their characteristics and diagnostic challenges. Furthermore, we review the current state-of-the-art ML techniques, such as support vector machines (SVM), K-Nearest Neighbor (KNN), and convolutional neural network (CNN), highlighting their strengths and limitations in skin cancer classification. A systematic search of academic databases, including Scopus, ResearchGate, Google Scholar, IEEE Xplore, Wiley Online Library, Elsevier, ScienceDirect, and Springer, was conducted. Continued evolution in skin cancer classification promises enhanced diagnostic accuracy and personalized treatment strategies.
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