International Journal of Scientific Research and Engineering Development

This project, “Kidney Care: Multimodal Kidney Diagnostics,” presents an innovative solution to the challenges of early and accurate Chronic Kidney Disease (CKD) diagnosis. It integrates two key components: a machine learning model for predicting CKD risk from patient health data [1], [7] and an image processing system for the automated detection and classification of kidney abnormalities, including tumors, cysts, and stones, from medical scans [6], [9]. The primary objective is to develop a comprehensive, automated system that acts as a decision-support tool for healthcare professionals. By combining data-driven insights with visual diagnostic analysis, the system aims to streamline the diagnostic process, reduce manual errors, and provide a reliable method for generating detailed patient reports [2], [4]. The project utilizes a robust technology stack, including Python, TensorFlow, Keras, and OpenCV, to deliver a solution that improves diagnostic efficiency and ultimately enhances the quality of patient care.

Shifa Bilal Tamboli, Simeen Phiroj Mulani, Arman Tajuddin Shiakh,"Kidney Care: Multimodal Kidney Diagnostic" International Journal of Scientific Research and Engineering Development, V9(2): Page(2167-2171) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.