An Intelligent CT Image Analysis System for Automated Detection of Kidney Stones Using Deep Learning

Kidney stone disease is a common ailment that needs to be diagnosed in due time and in an accurate manner to avoid extreme complications like obstruction of the kidney, infection and permanent damages to the kidney. Computed Tomography (CT) imaging is considered the gold standard of kidney stones detection as it has the high sensitivity and specificity. Manual interpretation of CT scans is difficult and time consuming however, it is likely to have inter-observer variation and extend a lot on the skills of radiologists. This paper seeks to solve these problems by coming up with an intelligent computerized tomography image analysis system to detect kidney stones automatically through deep learning methods. The paper uses a Convolutional Neural Network (CNN) that has been trained on a simulated set of labeled CT scans, which are positive and negative of kidney stones. Noise reduction, normalization, and data augmentation techniques were used to enhance the quality of the images and generalization of the model. The CNN model can automatically obtain hierarchical features of the images, which can be used to classify the images effectively without feature engineering. Accuracy, precision, recall, F1-score, specificity, and confusion matrix analysis of performance evaluation proved that the proposed system attained an average accuracy of 94.2, high sensitivity and low false negative rates. Comparative analysis indicated that CNN was better as compared to the traditional machine learning practices and the available models in the literature. The findings suggest that automated detection systems based on deep learning are capable of promoting efficiency in diagnostic, a decrease in workload of radiologists, and reliability in clinical decision-making. The paper offers a platform upon which future research can be undertaken on how best the incorporation of intelligent imaging systems can be integrated to real world medical practice especially in the healthcare settings, which are resource limited.

Kidney Stone Detection, Computed Tomography (CT), Deep Learning, Convolutional Neural Network (CNN)

1. Abdalla, P. A., Shakor, M. Y., Ameen, A. K., Mahmood, B. S., & Hama, N. R. (2025). CT imaging dataset for kidney stone AI detection. Data in Brief, 59, 111446. https://doi.org/10.1016/j.dib.2025.111446 [Google Scholar] [Crossref]

2. Adil, L., Al-Fatlawi, T., & Alsaeedi, A. (2024). Review of image segmentation techniques. Journal of Al-Qadisiyah for Computer Science and Mathematics, 16. https://doi.org/10.29304/jqcsm.2024.16.21613 [Google Scholar] [Crossref]

3. Akram, M., & Somani, B. (2025). Kidney stone disease: Epidemiology and management. Research Reports in Urology, 17, 449–459. https://doi.org/10.2147/RRU.S517758 [Google Scholar] [Crossref]

4. Alibabaei, S., Rahmani, M., Tahmasbi, M., Tahmasebi Birgani, M. J., & Razmjoo, S. (2023). GLCM texture features for glioblastoma treatment response. Journal of Medical Signals & Sensors, 13(4), 261–271. https://doi.org/10.4103/jmss.jmss_50_22 [Google Scholar] [Crossref]

5. Al-Khawari, H., Athyal, R. P., Al-Saeed, O., Sada, P. N., Al-Muthairi, S., & Al-Awadhi, A. (2010). Variability in interpreting lung changes on HRCT. Annals of Saudi Medicine, 30(2), 129–133. https://doi.org/10.4103/0256-4947.60518 [Google Scholar] [Crossref]

6. Anand, V., Khajuria, A., Pachauri, R. K., & Gupta, V. (2025). Optimized machine learning models for kidney tumor classification. Scientific Reports, 15(1), 30358. https://doi.org/10.1038/s41598-025-15414-w [Google Scholar] [Crossref]

7. Anderson, D., Ramachandran, P., Trapp, J., & Fielding, A. (2025). Deep learning for CT image analysis. Physics in Medicine and Biology, 48(4), 1491–1523. https://doi.org/10.1007/s13246-025-01635-w [Google Scholar] [Crossref]

8. Andrabi, Y., Patino, M., Das, C. J., Eisner, B., Sahani, D. V., & Kambadakone, A. (2025). Advances in CT imaging for kidney stones. Indian Journal of Urology, 31(3), 185–193. https://doi.org/10.4103/0970-1591.156924 [Google Scholar] [Crossref]

9. Böttcher, B., van Assen, M., Fari, R., von Knebel Doeberitz, P. L., Kim, E. Y., Berkowitz, E. A., Meinel, F. G., & De Cecco, C. N. (2025). Image retrieval for lung disease diagnosis in CT scans. European Radiology Experimental, 9(1), 4. https://doi.org/10.1186/s41747-024-00539-w [Google Scholar] [Crossref]

10. Brisbane, W., Bailey, M. R., & Sorensen, M. D. (2016). Imaging modalities for kidney stones. Nature Reviews Urology, 13(11), 654–662. https://doi.org/10.1038/nrurol.2016.154 [Google Scholar] [Crossref]

11. Dalla Mura, M., Benediktsson, J. A., Waske, B., & Bruzzone, L. (2020). Attribute profiles for high-resolution image analysis. IEEE Transactions on Geoscience and Remote Sensing, 48(10), 3747–3762. https://doi.org/10.1109/TGRS.2010.2048116 [Google Scholar] [Crossref]

12. Dawson, C. H., & Tomson, C. R. (2012). Kidney stones: Pathophysiology and treatment. Clinical Medicine, 12(5), 467–471. https://doi.org/10.7861/clinmedicine.12-5-467 [Google Scholar] [Crossref]

13. Elton, D. C., Turkbey, E. B., Pickhardt, P. J., & Summers, R. M. (2022). AI-based kidney stone detection in CT scans. Medical Physics, 49(4), 2545–2554. https://doi.org/10.1002/mp.15518 [Google Scholar] [Crossref]

14. Kachhava, R. (2025). AI in healthcare diagnostics: Implications and accuracy. International Research Journal of Modernization in Engineering Technology and Science, 7, 3901–3906. https://doi.org/10.56726/IRJMETS76850 [Google Scholar] [Crossref]

15. Lamé, G., & Dixon-Woods, M. (2020). Clinical simulation for healthcare quality improvement. BMJ Simulation & Technology Enhanced Learning, 6(2), 87–94. https://doi.org/10.1136/bmjstel-2018-000370 [Google Scholar] [Crossref]

16. Li, M., Jiang, Y., Zhang, Y., & Zhu, H. (2023). Deep learning for medical image analysis. Frontiers in Public Health, 11, 1273253. https://doi.org/10.3389/fpubh.2023.1273253 [Google Scholar] [Crossref]

17. Mienye, I. D., Swart, T. G., Obaido, G., Jordan, M., & Ilono, P. (2025). Convolutional neural networks for medical image analysis. Information, 16(3), 195. https://doi.org/10.3390/info16030195 [Google Scholar] [Crossref]

18. Paka, Mr. (2024). Image processing for kidney stone detection and analysis. International Journal for Research in Applied Science and Engineering Technology, 12, 3977–3982. https://doi.org/10.22214/ijraset.2024.62433 [Google Scholar] [Crossref]

19. Rashed, B. M., & Popescu, N. (2022). Review of medical image processing for disease evaluation. Sensors, 22(18), 7065. https://doi.org/10.3390/s22187065 [Google Scholar] [Crossref]

20. Singh, P., Mukundan, R., & De Ryke, R. (2020). Enhancing ultrasound video features with adaptive histogram equalization. Journal of Digital Imaging, 33(1), 273–285. https://doi.org/10.1007/s10278-019-00211-5 [Google Scholar] [Crossref]

21. Taassori, M. (2024). Wavelet and bilateral filter combo for image denoising. Sensors, 24(21), 6849. https://doi.org/10.3390/s24216849 [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet