International Journal of Science, Engineering and TechnologyAuthors: Om Dwivedi, Neelam Singh Parihar
Abstract: Skin cancer remains one of the most prevalent and potentially fatal forms of cancer worldwide, emphasizing the need for early and accurate detection. This systematic review explores recent advancements in machine learning (ML) techniques applied to skin cancer diagnosis. It examines supervised and deep learning models such as CNN, SVM, and ensemble classifiers across dermoscopic and clinical image datasets. The study highlights their effectiveness in lesion segmentation, classification, and feature extraction, achieving accuracy levels surpassing traditional diagnostic methods. Additionally, it discusses the challenges of data imbalance, model interpretability, and clinical deployment. The review concludes that integrating ML with explainable AI and multimodal imaging holds significant potential for improving diagnostic precision and supporting dermatologists in real-world decision-making.
