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Abstract Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring various diseases. Convolutional neural networks (CNNs) have become popular as they can extract intricate features and patterns from extensive datasets. The paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre‐trained models. The paper also discusses how transfer learning has been applied to different areas within medical image analysis. This comprehensive overview aims to assist researchers, clinicians, and policymakers by providing detailed insights, helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.
Wang et al. (Mon,) studied this question.