Study on Skin Cancer (SC) Detection Using Deep Learning (DL)Techniques: A Review

Abstract

Skin cancer is one of the most prevalent and life-threatening diseases, necessitating early and accurate detection for effective treatment. Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), have emerged as powerful tools in the automated detection and classification of skin cancer. This review examines the latest advancements in DL-based skin cancer detection, focusing on model architectures, performance metrics, and dataset utilization. Additionally, the study highlights key challenges such as data scarcity, model interpretability, and generalization issues. Future research directions, including multimodal data integration, transfer learning, and enhanced data augmentation techniques, are explored to improve diagnostic accuracy. The findings suggest that DL has significant potential in revolutionizing dermatological diagnostics by offering high precision, reduced subjectivity, and faster analysis.