Abstract: Kidney stone detection is a critical application in medical imaging aimed at aiding early diagnosis and treatment. This project presents a graphical user interface (GUI) application for automated kidney stone detection using image processing and machine learning techniques. Developed in Python, the system leverages libraries such as OpenCV, Tensor Flow, and Tkinter to create an intuitive, user-friendly tool for image analysis and classification.This tool demonstrates potential in assisting healthcare professionals with kidney stone detection, reducing manual effort and improving diagnostic accuracy. Future enhancements may include integrating real-time detection capabilities and expanding the classification model to cover additional medical imaging modalities. This project implements a kidney stone detection system using a graphical user interface (GUI) built with Python's Tkinter.
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DOI:
10.17148/IJARCCE.2025.14120
