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September 30, 2025Open Access

Nephrolithiasis Detection and Classification Based on Supervised Machine Learning

Key Points

Achieved 89.3% training accuracy using a cubic support vector machine with five-fold cross-validation.
Utilized computed tomography and advanced image processing techniques to enhance detection of renal calculi.
Involved segmentation, feature extraction, and machine learning for accurate classification of kidney stones.
Outlined a systematic approach to identify and classify nephrolithiasis based on CT scan data.

Abstract

In this paper, the author will provide an extensive exploration of the utilization of computed tomography (CT) image processing techniques for the detection of renal calculi. This is one of the most essential topics worldwide to detect the correct location of renal calculi. In the human system, the two kidneys play a crucial role in water purification and recycling. This research involves four steps: Graphic processing with a median filter, segmentation with the Otsu segmentation algorithm, nephrolithiasis detection, and discrete wavelet transform feature extraction and classification. Data from a large number of hospital patients were collected with CT scans, which diagnose renal calculi. This research studies advanced techniques to detect the extent, segment the area, and improve the detection of kidney stones or normal. This analysis helps locate the rocks through pixel analysis. The system also shows many stone patients. Specifically, the system was refined through a dataset of 1, 200 X-ray images, a cubic support vector machine achieved 89. 3% training accuracy with five-fold cross-validation to avoid overfitting, and an area under the curve close to 0. 85, and the receiver operating characteristic curve is close to one. Its outstanding performance on unseen data led to a 90% testing accuracy, demonstrating its robustness using MATLAB and Python IDLE simulator Received: 12 November 2024 | Revised: 3 March 2025 | Accepted: 10 June 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https: //github. com/junaid-1013/Kidney-Stone-Detection, https: //github. com/muhammedtalo/Kidneyₛtonedetection, and in Kaggle at https: //www. kaggle. com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone. Author Contribution Statement Ei Phyu Sin Win: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.

Nephrolithiasis Detection and Classification Based on Supervised Machine Learning

Key Points

Abstract

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