SmartUro: An Advanced Deep Learning Framework for Automated Kidney Stone Detection and Classification in Medical Imaging
Abstract
Kidney stone disease affects 10-15% of the global population, yet traditional diagnostic methods are time-intensive and error-prone. This paper presents SmartUro, an intelligent diagnostic system leveraging YOLOv8 deep learning architecture for automated kidney stone detection across CT, MRI, X-ray, and ultrasound imaging. Our system achieves 93.0% mean Average Precision (mAP50), with 93.8% precision and 92.9% recall, processing images in under 3 seconds. Through multi-dataset integration and systematic optimization, SmartUro demonstrates clinical-grade accuracy suitable for deployment in both well-resourced centers and underserved facilities. A Streamlit-based web interface enables real-time clinical integration with comprehensive diagnostic reporting.
Keywords
Kidney Stone Detection, YOLOv8, Deep Learning, Medical Image Analysis, Automated Diagnosis
Citations
IRE Journals:
Swaroop M, Nandan Gowda H M, Rakshitha P, Praveen Kamalappanavar, Satisha T "SmartUro: An Advanced Deep Learning Framework for Automated Kidney Stone Detection and Classification in Medical Imaging" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 2214-2219 https://doi.org/10.64388/IREV9I5-1712231
IEEE:
Swaroop M, Nandan Gowda H M, Rakshitha P, Praveen Kamalappanavar, Satisha T
"SmartUro: An Advanced Deep Learning Framework for Automated Kidney Stone Detection and Classification in Medical Imaging" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1712231
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