A Comprehensive Review on Deep Learning Methods in Medical Image Analysis
DOI:
https://doi.org/10.69968/ijisem.2025v4i438-41Keywords:
Deep learning, medical image analysis, convolutional neural networks, transformers, self-supervised learning, GANsAbstract
Deep learning (DL) has transformed medical image analysis over the past decade, enabling automated, accurate, and scalable solutions for detection, classification, segmentation, and synthesis of medical images. This review synthesizes the evolution of major deep learning architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformers and focuses on their specific applications in medical imaging modalities such as X-ray, CT, MRI, ultrasound, and histopathology. We discuss training strategies, data challenges, evaluation metrics, and clinical translation barriers. Finally, we present comparative tables, figure placeholders for common architectures, and an outlook on emerging directions including self-supervised learning, federated learning, and foundation models in medical imaging. The review includes key works from 2012–2025 to provide both foundational and contemporary context.
References
[1] Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., ... & Norouzi, M. (2021). Big self-supervised models advance medical image classification. arXiv preprint arXiv:2101.05224.https://doi.org/10.1109/ICCV48922.2021.00346
[2] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672-2680.
[3] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.https://doi.org/10.1109/CVPR.2016.90
[4] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.
[5] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.https://doi.org/10.1038/nature14539
[6] Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.https://doi.org/10.1016/j.media.2017.07.005
[7] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234-241.https://doi.org/10.1007/978-3-319-24574-4_28
[8] Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., ... & Ng, A. Y. (2017). CheXNet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225.
[9] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008.
[10] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12179-12190.
[11] Huang, S.-C., et al. (2023). Self-supervised learning for medical image classification: A systematic review. Journal of Medical Imaging and Health Informatics.https://doi.org/10.1038/s41746-023-00811-0
[12] Chan, H. P., et al. (2020). Deep learning in medical imaging: Overview and future promise. IEEE Transactions on Medical Imaging.
[13] Celard, P., et al. (2023). A survey on deep learning applied to medical images. Neural Computing and Applications.https://doi.org/10.1007/s00521-022-07953-4
[14] Haghighi, F., et al. (2024). Self-supervised learning in medical imaging: Survey and perspectives. Medical Image Analysis.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Lalit Kumar Rawat, Anil Kumar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Re-users must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. This license allows for redistribution, commercial and non-commercial, as long as the original work is properly credited.
How to Cite
7
