Skin Cancer Detection: A Journey Through Evolution and Emerging Trends

The field of skin cancer detection has experienced significant advancements, largely due to the incorporation of artificial intelligence (AI) techniques. This review traces the evolution of skin cancer detection methods, beginning with visual inspections and progressing through classical machine learning, deep learning, and collaborative initiatives. The essential contributions of the International Skin Imaging Collaboration (ISIC) in promoting data sharing and establishing benchmarks are emphasized. Insights gained from comparing the diagnostic abilities of human experts and AI algorithms underscore the unique strengths and challenges of each, fostering the development of synergistic human-machine collaborations. The review also delves into the enhanced accuracy and robustness provided by deep learning-based ensemble models. The rising significance of explainable AI is addressed, highlighting its role in enhancing the transparency and trustworthiness of AI systems. Additionally, the recent advent of vision transformers, which utilize attention mechanisms for superior image analysis and detection, is explored. This comprehensive overview offers a detailed understanding of the advancements in skin cancer detection, emphasizing its potential for early diagnosis and improved patient care, and outlines future research directions.