A Review on Deep Learning Applications in Medical Image Analysis

Xinyu Chen

doi:10.54254/2755-2721/2025.PO24941

Applied and Computational EngineeringOpen access

A Review on Deep Learning Applications in Medical Image Analysis

Research Article

Open Access

A Review on Deep Learning Applications in Medical Image Analysis

Xinyu Chen ^1*

¹ Institute of Computer Science and Technology, Beijing University of Posts and Telecommunications, BeiJing, China

^*Corresponding author: 2023211387@bupt.cn

Published on 10 July 2025

ACE Vol.174

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-235-5

ISBN (Online): 978-1-80590-236-2

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Abstract

In recent years, deep learning has demonstrated revolutionary impacts in medical image analysis due to its powerful feature learning and pattern recognition capabilities. This paper systematically reviews advances in core tasks and methodologies for medical image segmentation and classification. For segmentation tasks, this paper discusses how Fully Convolutional Networks (FCN) laid the foundation for pixel-level prediction, while their variants, such as the DeepLab series, optimized lesion segmentation accuracy through atrous convolution and multi-scale feature fusion. This research also covers U-Net and its 3D extensions (3D U-Net, V-Net), which significantly improved boundary consistency in organ segmentation by integrating skip connections and residual learning. Furthermore, this review examines Generative Adversarial Networks (SegAN, SCAN) and how they effectively addressed data scarcity and class imbalance through adversarial training. For classification tasks, this paper highlights classical convolutional neural networks like AlexNet, VGGNet, and ResNet, which achieved a paradigm shift from manual feature engineering to end-to-end learning via hierarchical feature abstraction and residual connections. Combined with transfer learning and multi-scale pooling strategies, these models substantially enhanced disease diagnosis accuracy and generalization. This review concludes that these technological breakthroughs have driven the transition of medical image analysis from traditional manual interpretation to intelligent, precise solutions, providing efficient and reliable support for clinical decision-making.

Keywords:

Deep Learning, Medical Image Segmentation, Medical Image Classification

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References

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References

[1]. Liu, X., Song, L., Liu, S., & Zhang, Y. (2021). A Review of Deep-Learning-Based Medical Image Segmentation Methods. Sustainability, 13(3), 1224. https: //doi.org/10.3390/su13031224

[3]. Ker, J., Wang, L., Rao, J., & Lim, T. (2018). Deep learning applications in medical image analysis. IEEE Access, 6, 9375–9389. https: //doi.org/10.1109/access.2017.2788044

[4]. Shen, D., Wu, G., & Suk, H.-I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19(1), 221–248. https: //doi.org/10.1146/annurev-bioeng-071516-044442

[6]. Suzuki, K. (2017). Overview of deep learning in medical imaging. Radiological Physics and Technology, 10(3), 257–273. https: //doi.org/10.1007/s12194-017-0406-5

[7]. Shen, D., Wu, G., & Suk, H.-I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19(1), 221–248. https: //doi.org/10.1146/annurev-bioeng-071516-044442

[22]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556. https: //doi.org/10.48550/arXiv.1409.1556

Cite this article

Chen,X. (2025). A Review on Deep Learning Applications in Medical Image Analysis. Applied and Computational Engineering,174,61-68.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Data Visualization Methods for Evaluatio

ISBN: 978-1-80590-235-5(Print) / 978-1-80590-236-2(Online)

Editor: Marwan Omar, Elisavet Andrikopoulou

Conference website: https://2025.confcds.org/portsmouth.html

Conference date: 30 July 2025

Series: Applied and Computational Engineering

Volume number: Vol.174

ISSN: 2755-2721(Print) / 2755-273X(Online)

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