Deep Learning in Medical Image Analysis: A Comprehensive Review of Algorithms, Trends, Applications, and Challenges

Dawa Chyophel Lepcha, Bhawna Goyal, Ayush Dogra, Ahmed Alkhayyat, Prabhat Kumar Sahu, Aaliya Ali, Vinay Kukreja,
Deep Learning in Medical Image Analysis: A Comprehensive Review of Algorithms, Trends, Applications, and Challenges,
CMES - Computer Modeling in Engineering and Sciences,
Volume 145, Issue 2,
2025,
Pages 1487-1573,
ISSN 1526-1492,
https://doi.org/10.32604/cmes.2025.070964.
(https://www.sciencedirect.com/science/article/pii/S1526149225004151)
Abstract: Medical image analysis has become a cornerstone of modern healthcare, driven by the exponential growth of data from imaging modalities such as MRI, CT, PET, ultrasound, and X-ray. Traditional machine learning methods have made early contributions; however, recent advancements in deep learning (DL) have revolutionized the field, offering state-of-the-art performance in image classification, segmentation, detection, fusion, registration, and enhancement. This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks, highlighting both foundational models and recent innovations. The article begins by introducing conventional techniques and their limitations, setting the stage for DL-based solutions. Core DL architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Vision Transformers (ViTs), and hybrid models, are discussed in detail, including their advantages and domain-specific adaptations. Advanced learning paradigms such as semi-supervised learning, self-supervised learning, and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets. This review further categorizes major tasks in medical image analysis, elaborating on how DL techniques have enabled precise tumor segmentation, lesion detection, modality fusion, super-resolution, and robust classification across diverse clinical settings. Emphasis is placed on applications in oncology, cardiology, neurology, and infectious diseases, including COVID-19. Challenges such as data scarcity, label imbalance, model generalizability, interpretability, and integration into clinical workflows are critically examined. Ethical considerations, explainable AI (XAI), federated learning, and regulatory compliance are discussed as essential components of real-world deployment. Benchmark datasets, evaluation metrics, and comparative performance analyses are presented to support future research. The article concludes with a forward-looking perspective on the role of foundation models, multimodal learning, edge AI, and bio-inspired computing in the future of medical imaging. Overall, this review serves as a valuable resource for researchers, clinicians, and developers aiming to harness deep learning for intelligent, efficient, and clinically viable medical image analysis.
Keywords: Medical image analysis; deep learning (DL); artificial intelligence (AI); neural networks; convolutional neural networks (CNNs); generative adversarial networks (GANs); transformers; natural language processing (NLP); computational applications; comprehensive analysis