Advancements in Deep Learning for Medical Image Analysis: A Comprehensive Review
Authors: ZHANG Wei, LI Ming, WANG Fang
Medical image analysis has witnessed a paradigm shift with the advent of deep learning techniques, which have demonstrated remarkable performance in tasks such as disease classification, lesion detection, and organ segmentation. This comprehensive review systematically examines the state-of-the-art deep learning methodologies applied to medical imaging, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). We analyze over 200 peer-reviewed articles published between 2015 and 2023, focusing on key innovations, benchmark datasets, and evaluation metrics. Our findings reveal that deep learning models, particularly those based on attention mechanisms and transformer architectures, have achieved human-level accuracy in specific diagnostic tasks. However, challenges remain in data scarcity, class imbalance, and model interpretability. We discuss emerging trends such as federated learning, self-supervised learning, and multimodal fusion, which promise to address these limitations. Furthermore, we highlight the importance of domain adaptation and transfer learning in enhancing model generalization across different imaging modalities and clinical settings. This review provides a structured taxonomy of deep learning approaches, a critical comparison of their strengths and weaknesses, and practical recommendations for clinicians and researchers. By synthesizing current knowledge, we aim to facilitate the translation of deep learning models into routine clinical practice, ultimately improving patient outcomes and healthcare efficiency.
