SmartUro: An Advanced Deep Learning Framework for Automated Kidney Stone Detection and Classification in Medical Imaging

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@article{1712231,
      author = {Swaroop M, Nandan Gowda H M, Rakshitha P, Praveen Kamalappanavar, Satisha T},
      title = {SmartUro: An Advanced Deep Learning Framework for Automated Kidney Stone Detection and Classification in Medical Imaging},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {2214-2219},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712231.pdf},
      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},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1712231}
  }