A Review of Skin Cancer Detection: Traditional and Deep Learning-Based Techniques

Abstract

One of the most serious types of cancer is skin cancer. The rising number of skin cancer cases, high mortality rate, and high cost of medical treatment necessitate early detection of its symptoms. Skin cancer is detected and differentiated from melanoma using lesion criteria such as symmetry, color, size, and shape.

Given the significance of these challenges, researchers have developed a variety of early-detection approaches for skin cancer.

This paper comprehensively reviews classical and deep-learning techniques for detecting early skin cancer. The performance of these techniques is evaluated based on various metrics, and the datasets used for training and testing are analyzed. Studies using techniques such as clinical examination, dermoscopy, and histopathology are identified, and the architecture of the deep neural networks used for skin cancer detection is analyzed. A comprehensive comparison of classical and deep-learning techniques for skin cancer detection is provided in this review paper.