Machine Learning based Automated Detection of Kidney Stones from CT Scan Images for Enhanced Diagnostic Accuracy

Authors

  • Sandeep Lather, Sandeep

Keywords:

Kidney Stone, CT Scan images, Machine Learning, Deep Learning

Abstract

This research presents a machine learning-based approach for the automatic detection of kidney stones using CT scan images, aiming to enhance diagnostic accuracy and reduce the reliance on manual interpretation by radiologists. Kidney stones, if left undiagnosed, can lead to severe complications such as infections, renal failure, or urinary tract blockages. The study utilized a labeled dataset of CT images categorized as normal and stone-affected, which underwent preprocessing, feature extraction, and classification using various machine learning models, including Support Vector Machine (SVM), Random Forest, Decision Tree, Naïve Bayes, and K-Nearest Neighbors (KNN). Among these, SVM achieved the highest accuracy of 93%, followed closely by Random Forest at 91%, demonstrating their effectiveness in correctly identifying kidney stone cases. The proposed system shows strong potential in assisting healthcare professionals by improving diagnostic efficiency, reducing errors, and enabling timely medical intervention.

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Published

17.12.2024

How to Cite

Sandeep Lather. (2024). Machine Learning based Automated Detection of Kidney Stones from CT Scan Images for Enhanced Diagnostic Accuracy. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 5771 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7623

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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