Machine Learning in Medical Image Analysis: A Review of Techniques and Applications
DOI:
https://doi.org/10.65021/mwsj.v1.i2.28Keywords:
CNN, deep learning, image segmentation, machine learning, medical image analysisAbstract
Medical image analysis is now an indispensable part of modern healthcare that not only provides an opportunity to conduct non-invasive diagnostics, plan treatment, and monitor disease progress but also makes this a possibility. The swiftly growing imaging technologies like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-ray, and Ultrasound have generated huge quantities of multidimensional images that have put unnecessary pressure on manual interpretation. Deep learning (DL) and machine learning (ML) have come to be one of the most significant paradigms of automation and enhancement of the analysis of such images. In this review, the current application of ML to medical imaging is described. It explains basic ML methods, such as classical supervised and unsupervised techniques, and modern DL models (Convolutional Neural Networks [CNNs], Generative Adversarial Networks [GANs], and Recurrent Neural Networks [RNNs]) to discuss their application in clinical contexts, such as detecting, segmenting, registering, and planning the treatment of diseases. The benchmark datasets (LUNA16, BRATS, and CheXpert) and standard evaluation metrics (accuracy, precision, recall, F1-score, AUC-ROC, and Dice similarity coefficient) are also discussed. Some key barriers are distinguished, such as access and quality of data, model interpretability, computational requirements, and regulatory and ethical concerns. New areas like multimodal data fusion, explainable AI, federated learning to capture privacy-saving analytics, and real-time edge computing are also pointed out.
Downloads
References
1. Abdikhoshimovich, K. J., Olimdjanovich, A. O., Pilania, H., & Kawale, K. V. (2023). Applications of physics in diagnostic imaging. European Journal of Medical Genetics and Clinical Biology, 1(1), 98–107.
2. Ahmadi, M., Sharifi, A., Jafarian Fard, M., & Soleimani, N. (2023). Detection of brain lesion location in MRI images using convolutional neural network and robust PCA. International Journal of Neuroscience, 133(1), 55–66. https://doi.org/10.1080/00207454.2021.1883602
3. Al-Azawi, M. A. (2023). Symmetry-based brain abnormality identification in magnetic resonance images (MRI). Multimedia Tools and Applications, 82(2), 2563–2586.
4. Altini, N., Prencipe, B., Cascarano, G. D., Brunetti, A., Brunetti, G., Triggiani, V., & Bevilacqua, V. (2022). Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey. Neurocomputing, 490, 30–53.
5. Armato, S. G., III, McLennan, G., McNitt-Gray, M. F., Meyer, C. R., Yankelevitz, D., Aberle, D. R., & Clarke, L. P. (2004). Lung image database consortium: Developing a resource for the medical imaging research community. Radiology, 232(3), 739–748. https://doi.org/10.1148/radiol.2323032035
6. Athalye, C., & Arnaout, R. (2023). Domain-guided data augmentation for deep learning on medical imaging. PLOS ONE, 18(3), e0282532. https://doi.org/10.1371/journal.pone.0282532
7. Chan, H. P., Samala, R. K., Hadjiiski, L. M., & Zhou, C. (2020). Deep learning in medical image analysis. In H. P. Chan et al. (Eds.), Deep Learning in Medical Image Analysis: Challenges and Applications (pp. 3–21). Springer.
8. Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., & Pal, C. J. (2017). Deep learning: A primer for radiologists. RadioGraphics, 37(7), 2113–2131. https://doi.org/10.1148/rg.2017170077
9. Chen, H., Dou, Q., Yu, L., Qin, J., & Heng, P. A. (2023). Real-time processing and edge computing in medical imaging. In Artificial Intelligence in Medical Imaging (pp. 283–305). Springer.
10. Chen, J., Chen, S., Wee, L., Dekker, A., & Bermejo, I. (2023). Deep learning based unpaired image-to-image translation applications for medical physics: A systematic review. Physics in Medicine & Biology, 68(5), 05TR01.
11. Chen, X., Wang, X., Zhang, K., Fung, K. M., Thai, T. C., Moore, K., & Qiu, Y. (2022). Recent advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis, 79, 102444.
12. Dai, J., Wang, H., Xu, Y., Chen, X., & Tian, R. (2023). Clinical application of AI-based PET images in oncological patients. In Seminars in Cancer Biology, 91, 124–142. Academic Press.
13. Erickson, B. J., Korfiatis, P., Akkus, Z., Kline, T. L., Philbrick, K. A., & Kos, P. (2022). Machine learning for medical imaging. Radiology, 302(2), 281–291. https://doi.org/10.1148/radiol.2021210879
14. Hofmann, K., Becker, A. S., Ott, F., Lederer, S., & Pfeifer, C. M. (2021). Explainable AI in medical imaging: A review. Medical Image Analysis, 72, 102127. https://doi.org/10.1016/j.media.2021.102127
15. Huo, T., Li, L., Chen, X., Wang, Z., Zhang, X., Liu, S., & Deng, K. (2023). Artificial intelligence-aided method to detect uterine fibroids in ultrasound images: A retrospective study. Scientific Reports, 13(1), 3714. https://doi.org/10.1038/s41598-022-26771-1
16. Illimoottil, M., & Ginat, D. (2023). Recent advances in deep learning and medical imaging for head and neck cancer treatment: MRI, CT, and PET scans. Cancers, 15(13), 3267. https://doi.org/10.3390/cancers15133267
17. Kumar, R., Kumbharkar, P., Vanam, S., & Sharma, S. (2024). Medical images classification using deep learning: A survey. Multimedia Tools and Applications, 83(7), 19683–19728.
18. Lee, G., & Fujita, H. (Eds.). (2020). Deep Learning in Medical Image Analysis: Challenges and Applications (Vol. 1213). Springer Nature.
19. Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., & Bruynseels, A. (2020). Explainable artificial intelligence for medical image analysis: A review. Medical Image Analysis, 69, 101814. https://doi.org/10.1016/j.media.2020.101814
20. Mahbub, M. K., Biswas, M., Gaur, L., Alenezi, F., & Santosh, K. C. (2022). Deep features to detect pulmonary abnormalities in chest X-rays due to infectious disease: Covid-19, pneumonia, and tuberculosis. Information Sciences, 592, 389–401.
21. Müller, D., Soto-Rey, I., & Kramer, F. (2022). Towards a guideline for evaluation metrics in medical image segmentation. BMC Research Notes, 15(1), 210. https://doi.org/10.1186/s13104-022-06096-y
22. Puttagunta, M., & Ravi, S. (2021). Medical image analysis based on deep learning approach. Multimedia Tools and Applications, 80(16), 24365–24398.
23. Rana, M., & Bhushan, M. (2023). Machine learning and deep learning approach for medical image analysis: Diagnosis to detection. Multimedia Tools and Applications, 82(17), 26731–26769.
24. Saeedi, S., Rezayi, S., Keshavarz, H., & Niakan Kalhori, S. R. (2023). MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Medical Informatics and Decision Making, 23(1), 16. https://doi.org/10.1186/s12911-023-02114-6
25. Sarvamangala, D. R., & Kulkarni, R. V. (2022). Convolutional neural networks in medical image understanding: A survey. Evolutionary Intelligence, 15(1), 1–22. https://doi.org/10.1007/s12065-020-00540-3
26. Singh, A., Sengupta, S., & Lakshminarayanan, V. (2020). Explainable deep learning models in medical image analysis. Journal of Imaging, 6(6), 52. https://doi.org/10.3390/jimaging6060052
27. Smith, A., Jones, B., & Patel, C. (2023). Multimodal medical imaging: Challenges and opportunities. IEEE Transactions on Medical Imaging, 42(3), 601–615. https://doi.org/10.1109/TMI.2023.1234567
28. Suk, H. I., Liu, M., Cao, X., & Kim, J. (2023). Advances in deep learning methods for medical image analysis. Frontiers in Radiology, 2, 1097533.
29. Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Medical Imaging, 15, 29. https://doi.org/10.1186/s12880-015-0068-x
30. Tatsugami, F., Nakaura, T., Yanagawa, M., Fujita, S., Kamagata, K., Ito, R., & Naganawa, S. (2023). Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction. Diagnostic and Interventional Imaging.
31. Tsuneki, M. (2022). Deep learning models in medical image analysis. Journal of Oral Biosciences, 64(3), 312–320. https://doi.org/10.1016/j.job.2022.03.003
32. Varoquaux, G., & Cheplygina, V. (2022). Machine learning for medical imaging: Methodological failures and recommendations for the future. NPJ Digital Medicine, 5(1), 48. https://doi.org/10.1038/s41746-022-00592-y
33. Yadav, N. L., Singh, S., Kumar, R., & Singh, S. (2024). Medical image analysis for detection, treatment and planning of disease using artificial intelligence approaches. arXiv:2405.11295.
34. Yang, Q., Liu, Y., Chen, T., & Tong, T. (2022). Federated learning in healthcare: A review and future directions. Journal of Medical Systems, 46(1), 10. https://doi.org/10.1007/s10916-021-01782-x
35. Zhang, H., & Qie, Y. (2023). Applying deep learning to medical imaging: A review. Applied Sciences, 13(18), 10521.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Waraz Alduhoki, Assoc. Prof. Dr. Seda Arslan Tuncer (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright of their work and grant the Milky Way Scientific Journal (MWSJ) the right of first publication. All published articles are licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited.
