Classification of X-Ray Images Using CNN Models

Abstract

Among medical imaging systems that play a crucial role in modern medical diagnosis and treatment processes, X-ray imaging stands out as an essential diagnostic tool due to its low cost and wide accessibility. This study focuses on developing a model based on a Convolutional Neural Network (CNN) architecture to automatically identify and classify anatomical regions in X-ray images. Using the MURA dataset and the UNIFESP X-Ray Body Part Classification dataset obtained from Kaggle, detailed anatomical region and projection view classification was performed on 7,487 multi-view musculoskeletal radiographs. The classification process utilized the AlexNet and ResNet50 architectures. To enhance the transparency and interpretability of the decision mechanisms, visual analysis was conducted using the Grad-CAM technique on misclassified samples. The obtained results showed that the AlexNet model achieved a validation accuracy of 91.52%, while the ResNet50 model achieved 94.20%. These findings demonstrate that detailed anatomical and directional classification can be performed with high accuracy, suggesting that this method could serve as an effective approach to improving labelling accuracy in hospital information systems.

Keywords

• AlexNet
• ResNet50
• datasets
• pacs
• Grad-CAM

References

1. D. Comaniciu, K. Engel, B. Georgescu, and T. Mansi, “Shaping the future through innovations: From medical imaging to precision medicine,” Medical Image Analysis, vol. 33, pp. 19–26, 2016. DOI: 10.1016/j.media.2016.06.016.
2. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017. DOI:10.1145/3065386.
3. E. Avuçlu, “Examining the effect of pre-processed Covid-19 images on classification performance using deep learning method,” International Scientific and Vocational Studies Journal, vol. 7, no. 2, pp. 94–102, 2023. DOI: 10.47897/bilmes.1359954.
4. A. Saygılı, “Classification and diagnostic prediction of breast cancers via different classifiers,” International Scientific and Vocational Studies Journal, vol. 2, no. 2, pp. 48–56, 2018.
5. H. Güven and A. Saygılı, “Monkeypox diagnosis using MRMR-based feature selection and hybrid deep learning models: ResNet50V2, NASNetMobile, and InceptionV3,” International Scientific and Vocational Studies Journal, vol. 9, no. 1, pp. 173–182, 2025. DOI: 10.47897/bilmes.1706322.
6. F. A. Mohammed, K. K. Tune, B. G. Assefa, M. Jett, and S. Muhie, “Medical image classifications using convolutional neural networks: A survey of current methods and statistical modeling of the literature,” Machine Learning & Knowledge Extraction, vol. 6, no. 1, pp. 699–735, 2024. DOI: 10.3390/make6010033.
7. H. K. Huang, PACS and Imaging Informatics: Basic Principles and Applications. Hoboken, NJ: Wiley-Blackwell, 2010.
8. P. Rajpurkar et al., “MURA: Large dataset for abnormality detection in musculoskeletal radiographs,” In Proc. 1st Conference on Medical Imaging with Deep Learning (MIDL), 2017, arXiv:1712.06957. DOI: 10.48550/arXiv.1712.06957.

9. K. Raghesh Krishnan and S. Padmavathi, “Body part classification from Gabor enhanced x-Ray images using deep convolutional models,” Procedia Comput Science, vol. 260, pp. 101–109, 2025. DOI: 10.1016/j.procs.2025.03.182.
10. P. Rajpurkar et al., “CheXNet: Radiologist-level pneumonia detection on Chest X-rays with deep learning,” In Proc. Conference on Computer Vision and Pattern Recognition, 2017, arXiv:1711.05225. DOI: 10.48550/arXiv.1711.05225.
11. X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” In Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3462–3471. DOI: 10.1109/CVPR.2017.369.
12. I. M. Baltruschat, H. Nickisch, M. Grass, T. Knopp, and A. Saalbach, “Comparison of deep learning approaches for multi-label Chest x-ray classification,” Scientific Reports, vol. 9, e 6381, 2019. DOI: 10.1038/s41598-019-42294-8.
13. S. Langer, J. Ritter, R. Braren, R. Daniel, P. Hager, “Self-supervised radiograph anatomical region classification how clean is your real-world data?,” 2025, arXiv:2412.15967. DOI: 10.48550/arXiv.2412.15967.
14. S. Lu, Z. Lu, and Y. D. Zhang, “Pathological brain detection based on AlexNet and transfer learning,” Journal of Computational Science, vol. 30, pp. 41–47, 2019. DOI: 10.1016/j.jocs.2018.11.008.
15. M. B. Hossain, S. M. H. S. Iqbal, M. M. Islam, M. N. Akhtar, and I. H. Sarker, “Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images,” Informatics in Medicine Unlocked, vol. 30, article 100916, 2022. DOI: 10.1016/j.imu.2022.100916.
16. F. Garcea, A. Serra, F. Lamberti, and L. Morra, “Data augmentation for medical imaging: A systematic literature review,” Computers in Biology and Medicine, vol. 152, article 106391, 2023. DOI: 10.1016/j.compbiomed.2022.106391.
17. R. Raina, A. Madhavan, and A. Y. Ng, “Large-scale deep unsupervised learning using graphics processors,” In Proc. ICML’ 09: The 26th Annual International Conference on Machine Learning, 2009, pp. 873–880. DOI: 10.1145/1553374.1553486.
18. V. Sze, Y.-H. Chen, T-J. Yang, J. S. Emer, “Efficient processing of deep neural networks: A tutorial and survey,” Proceedings of the IEEE, vol. 105, no. 2, pp. 2295-2329, 2017. DOI: 10.1109/JPROC.2017.2761740.
19. M. M. Sufian et al., “COVID-19 classification through deep learning models with three-channel grayscale CT images,” Big Data and Cognitive Computing, vol. 7, no. 1, e 36, 2023. DOI: 10.3390/bdcc7010036.
20. S. R. Yang, H. C. Yang, F. R. Shen, and J. Zhao, “Image data augmentation for deep learning: A survey,” Ruan Jian Xue Bao/Journal of Software, vol. 36, no. 3, pp. 1390–1412, 2022. DOI: 10.13328/j.cnki.jos.007263.
21. S. Albahli and G. N. A. H. Yar, “Efficient grad-cam-based model for COVID-19 classification and detection,” Computer Systems Science and Engineering, vol. 44, no. 3, pp. 2743–2757, 2022. DOI: 10.32604/csse.2023.024463.
22. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” In Proc. IEEE International Conference on Computer Vision (ICCV), 2017, pp. 618–626. DOI: 10.1109/ICCV.2017.74.
23. X. Cai et al., “Sound event detection on grad-¬CAM method with complex pretrained model”, In Proc. 2nd IEEE International Conference on Computer Graphics, Image and Virtualization (ICCGIV), 2022, pp. 94–97.