Review on Automated Skin Cancer Detection Using Image Processing Techniques

Abstract

Skin cancer is one of the most common forms of cancer worldwide, and early detection is critical for improving survival rates, especially for melanoma, the deadliest type of skin cancer. Melanoma, if detected early, has a high survival rate, making accurate diagnostic tools essential. This paper presents a computer-aided diagnostic system that utilizes advanced image processing techniques to detect skin cancer, particularly melanoma. The proposed system analyzes skin lesion images using the ABCD method (Asymmetry, Border irregularity, Color variation, and Diameter) to classify lesions as benign or malignant. This review also discusses the methods, databases, and algorithms used in automated skin cancer detection, offering valuable insights for researchers focused on improving early detection techniques.