International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Based Kidney Stone Detection Using Deep Learning and Ultrasound Images |
|---|---|
| ABSTRACT | Kidney stone disease is one of the most common urinary tract disorders affecting millions of people worldwide. Early diagnosis is essential to prevent severe complications such as kidney damage and urinary obstruction. Conventional diagnosis using ultrasound images requires expert interpretation and is often affected by image noise and human error. This paper presents an Artificial Intelligence-based Kidney Stone Detection System using Deep Learning techniques for automatic classification of kidney ultrasound images. The proposed system utilizes a Convolutional Neural Network (CNN) model implemented using TensorFlow and Keras to classify ultrasound images into Normal Kidney and Kidney Stone categories. Image preprocessing techniques including resizing, normalization, and noise reduction are applied before training the model. A Streamlit-based web application is developed to provide an interactive interface where users can upload ultrasound images and instantly receive prediction results with confidence scores. Experimental results demonstrate that the proposed system achieves high prediction accuracy while reducing manual diagnostic effort. The developed application provides a fast, reliable, and user-friendly solution that can assist healthcare professionals in early kidney stone diagnosis. |
| AUTHOR | LOGESHWARAN G, V.GANGA Student, Department of Computer Science and Engineering, Sri Venkateswara College of Engineering and Technology, Thiruvallur, Tamil Nadu, India Assistant Professor, Department of Computer Science and Engineering, Sri Venkateswara College of Engineering and Technology, Thiruvallur, Tamil Nadu, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.14060112 |
| pdf/112_AI-Based Kidney Stone Detection Using Deep Learning and Ultrasound Images.pdf | |
| KEYWORDS | |
| References | 1. Elton, D. C., Turkbey, E. B., Pickhardt, P. J., & Summers, R. M. (2022). A deep learning system for automated kidney stone detection and volumetric segmentation on non-contrast CT scans. Medical Physics, 49(4), 2545–2554. https://doi.org/10.1002/mp.15518 2. Parakh, A., Lee, H., Lee, J. H., Eisner, B. H., Sahani, D. V., & Do, S. (2019). Urinary stone detection on CT images using deep convolutional neural networks: Evaluation of model performance and generalization. Radiology: Artificial Intelligence, 1(4), e180066. https://doi.org/10.1148/ryai.2019180066 3. Elhaie, M., Koozari, A., Albeshan, S. M., & Mozafari, M. H. (2026). Applications of artificial intelligence algorithms in ultrasound-based kidney stone detection, classification, prediction, and management: A systematic review. Abdominal Radiology. Advance online publication. https://doi.org/10.1007/s00261-026-05419-y 4. Ravisankar, P., Balaji, V., & Hameed, S. T. (2024). Deep learning-based renal stone detection: A comprehensive study and performance analysis. Applied Computer Systems, 29(1), 112–116. https://doi.org/10.2478/acss-2024-0014 5. Khan, A., Das, R., & Parameshwara, M. C. (2022). Detection of kidney stone using digital image processing: A holistic approach. Engineering Research Express, 4. https://doi.org/10.1088/2631-8695/ac8b65 6. Nandini, Bhadrashetty, A., & Bhadrashetty, M. (2026). AI-based kidney stone detection using ultrasound imaging. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2026.83457 7. Lal, B., Reddy, S. K., Bhaskar, A., Bhaskar, M., Ashish, & Bisen, D. (2025). CNN: Deep learning model based kidney stone detection using image processing. Journal of Information Systems Engineering and Management, 10(31s). https://doi.org/10.52783/jisem.v10i31s.5123 8. Ahmad, F., Ishtiaq, U., & Shahid, M. M. A. (2026). Multi-phase CNN-ViT-wavelet fusion with attention for robust kidney stone detection from ultrasound images. Inspire Intelligence Journal. https://doi.org/10.65718/inspireAI.2026.1008 9. Litjens, G., Kooi, T., Bejnordi, B. E., et al. (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 10. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of MICCAI 2015 (pp. 234–241). Springer. https://doi.org/10.1007/978-3-319-24574-4_28 11. 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 (pp. 770–778). 12. Tan, M., & Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (pp. 6105–6114). 13. Dosovitskiy, A., et al. (2021). An image is worth 16×16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (ICLR). |
