Open Access

REVIEW

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

Dawa Chyophel Lepcha^1,*, Bhawna Goyal^2,3, Ayush Dogra⁴, Ahmed Alkhayyat⁵, Prabhat Kumar Sahu⁶, Aaliya Ali⁷, Vinay Kukreja⁴

1 Symbiosis Artificial Intelligence Institute, Symbiosis International (Deemed University), Pune, 412115, Maharashtra, India
2 Department of Electronics and Communication Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi University, Rajkot, 360003, Gujarat, India
3 Faculty of Engineering, Sohar University, Sohar, 311, Oman
4 Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India
5 College of Technical Engineering, The Islamic University, Najaf, 54001, Iraq
6 Department of Computer Science and Information Technology, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030, Odisha, India
7 Department of Biotechnology, University Centre for Research and Development, Chandigarh University, Mohali, 140413, Punjab, India

* Corresponding Author: Dawa Chyophel Lepcha. Email:

(This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)

Computer Modeling in Engineering & Sciences 2025, 145(2), 1487-1573. https://doi.org/10.32604/cmes.2025.070964

Received 28 July 2025; Accepted 24 October 2025; Issue published 26 November 2025

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.

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