Open Access

REVIEW

Deep Learning in Biomedical Image and Signal Processing: A Survey

Batyrkhan Omarov^1,2,3,4,*

1 School of Digital Technologies, Narxoz University, Almaty, 050035, Kazakhstan
2 Department of Mathematical and Computer Modeling, International Information Technology University, Almaty, 050040, Kazakhstan
3 Department of Cybersecurity and Cryptology, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
4 Department of Software Engineering, Faculty of Physics, Mathematics and Information Technology, Khalel Dosmukhamedov Atyrau University, Atyrau, 060011, Kazakhstan

* Corresponding Author: Batyrkhan Omarov. Email:

(This article belongs to the Special Issue: Emerging Trends and Applications of Deep Learning for Biomedical Signal and Image Processing)

Computers, Materials & Continua 2025, 85(2), 2195-2253. https://doi.org/10.32604/cmc.2025.064799

Received 24 February 2025; Accepted 31 July 2025; Issue published 23 September 2025

Abstract

Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing, enabling automated lesion detection, physiological monitoring, and therapy planning with accuracy that rivals expert performance. This survey reviews the principal model families as convolutional, recurrent, generative, reinforcement, autoencoder, and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation, classification, reconstruction, and anomaly detection. A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust, context-aware predictions. To support clinical adoption, we outline post-hoc explainability techniques (Grad-CAM, SHAP, LIME) and describe emerging intrinsically interpretable designs that expose decision logic to end users. Regulatory guidance from the U.S. FDA, the European Medicines Agency, and the EU AI Act is summarised, linking transparency and lifecycle-monitoring requirements to concrete development practices. Remaining challenges as data imbalance, computational cost, privacy constraints, and cross-domain generalization are discussed alongside promising solutions such as federated learning, uncertainty quantification, and lightweight 3-D architectures. The article therefore offers researchers, clinicians, and policymakers a concise, practice-oriented roadmap for deploying trustworthy deep-learning systems in healthcare.

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