Currently submitted to: JMIR Bioinformatics and Biotechnology
Date Submitted: Nov 19, 2025
Open Peer Review Period: Dec 8, 2025 - Feb 2, 2026
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A Comprehensive Review of Deep Learning in Medical Image Analysis
ABSTRACT
Background:
The paradigm of Deep Learning (DL) has fundamentally transformed Medical Image Analysis (MIA), offering automated, objective, and highly accurate solutions for intricate diagnostic challenges. Traditional computer vision methods are often limited by manual feature engineering, a constraint overcome by DL techniques, specifically Convolutional Neural Networks (CNNs), which automatically learn hierarchical features from raw image data.
Objective:
This comprehensive review aims to systematically cover the current state of DL in MIA. The primary objectives are to detail foundational model architectures, review their applications across various imaging modalities, analyze the critical challenge of data scarcity in medical contexts, and discuss advanced techniques and future directions intended to overcome these limitations.
Methods:
This paper is a narrative review. It systematically examines foundational DL model architectures (including CNNs, U-Net, and Transformers) and key applications (classification, segmentation, detection, and reconstruction) in MIA. It synthesizes literature concerning the practical solutions deployed to address the challenge of limited labeled medical data, such as Transfer Learning (TL), semi-supervised learning, and the use of Generative Adversarial Networks (GANs). The review concludes by analyzing contemporary challenges and emerging trends.
Results:
DL models have demonstrated superior performance across core MIA tasks compared to traditional methods. Practical solutions like Transfer Learning and model specialization (e.g., U-Net for segmentation) are essential for boosting diagnostic accuracy in data-limited settings. Furthermore, advanced approaches like synthetic data generation (GANs) and collaborative learning methods are key to improving robustness and efficiency in real-world clinical environments.
Conclusions:
While DL has significantly advanced MIA, key challenges remain, notably the interpretability (the "black box" problem) of model decisions, the necessity for better generalization across diverse hospital settings, and ethical considerations surrounding data privacy. Future research is focused on developing Explainable AI (XAI) methods, personalized medicine applications, and privacy-friendly collaborative learning via techniques like Federated Learning. Clinical Trial: N/A
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