Automated skin cancer diagnosis using deep learning: a systematic review of state-of-the-art architectures, techniques and performance evaluation

This literature survey offers a comprehensive analysis of deep learning techniques for skin cancer diagnosis. Prompt identification is crucial for improving patient survival rates, and deep learning has demonstrated promising results. The survey examines the fundamentals of skin cancer, various neural network architectures, and their classification efficacy. It investigates the application of deep learning models in clinical decision-making and assesses authentic datasets for evaluating skin cancer detection techniques. Training strategies for enhancing deep learning models are delineated. The survey assesses essential performance indicators, including accuracy, precision, recall, and F1-score. This survey underscores the growing importance of deep learning in skin cancer diagnosis, demonstrating its potential to improve the patient experience and advance clinical practice.