Skin Cancer Detection

Abstract

Skin cancer, particularly melanoma, is among the most common and dangerous types of cancer, responsible for significant morbidity and mortality worldwide. Early detection of skin cancer is critical for improving survival rates, but conventional diagnostic methods, such as visual inspection, dermoscopy, and biopsy, come with limitations in accuracy, accessibility, and speed. Recent advancements in machine learning (ML) and deep learning (DL) have shown great promise in automating the detection process, offering faster, more accurate diagnoses. This study explores the application of convolutional neural networks (CNNs), one of the most effective ML models, for detecting skin cancer from dermatological images. The paper provides an overview of key ML techniques, compares them to traditional diagnostic methods, and discusses the challenges, benefits, and potential for integration into clinical practice.