Advanced Deep Learning Architectures for Medical Image Analysis: Encoder-Decoder, Transformer, and Hybrid Networks

Recent advances in deep learning have significantly transformed medical image analysis by enabling highly accurate automated diagnosis, segmentation, and clinical decision support. Although convolutional neural networks (CNNs) have served as the foundation of many medical imaging systems, their limited ability to model long-range contextual information and complex anatomical relationships has motivated the development of more advanced neural network architectures. Encoder-decoder models, transformer-based networks, and hybrid architectures have emerged as powerful alternatives capable of addressing these limitations while improving diagnostic performance across diverse clinical applications. This review provides a comprehensive overview of advanced deep learning architectures that have become increasingly influential in modern medical image analysis. Encoder-decoder models, including U-Net, U-Net++, and Attention U-Net, are discussed with emphasis on their segmentation capabilities and clinical applications. Transformer-based architectures, particularly Vision Transformer (VIT) and Swin Transformer, are reviewed for their ability to capture global contextual information and improve image understanding. Furthermore, hybrid CNN-Transformer networks and recurrent models such as Long Short-Term Memory (LSTM) are examined to illustrate how combining complementary architectural strengths can enhance performance in multimodal and sequential medical data analysis. A comparative analysis is presented by evaluating these architectures in terms of segmentation accuracy, computational complexity, data requirements, interpretability, and clinical applicability. Current challenges, including limited annotated datasets, high computational demands, model interpretability, and generalizability across healthcare institutions, are also discussed. Finally, emerging research directions involving foundation models, self-supervised learning, multimodal artificial intelligence, federated learning, and explainable AI are highlighted as promising strategies for the next generation of intelligent healthcare systems. This review aims to provide researchers and clinicians with a structured understanding of contemporary deep learning architectures and practical guidance for selecting appropriate models for different medical imaging applications.

کلیدواژه ها:

Medical Image Analysis ، Deep Learning ، U-Net ، Vision Transformer ، Hybrid Networks ، Artificial Intelligence