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International Journal of Advanced Research in Computer and Communication Engineering A monthly Peer-reviewed & Refereed journal
ISSN Online 2278-1021ISSN Print 2319-5940Since 2012
IJARCCE adheres to the suggestive parameters outlined by the University Grants Commission (UGC) for peer-reviewed journals, upholding high standards of research quality, ethical publishing, and academic excellence.
← Back to VOLUME 14, ISSUE 1, JANUARY 2025

Analysis Of Health Symptoms To Identify Renal Stones

Mr. NARASIMHARAJU PAKA, B. SHIRISHA REDDY, SHAZIYA.U

DOI: 10.17148/IJARCCE.2025.14120

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.

How to Cite:

[1] Mr. NARASIMHARAJU PAKA, B. SHIRISHA REDDY, SHAZIYA.U, “Analysis Of Health Symptoms To Identify Renal Stones,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.14120