Authors - Rajlaxmi Sunil Sangve, Riya Jha, Bhagyashri Narale, Sakshi Hosamani Abstract - Kidney disease is an asymptomatic disease, which leads to severe complications or even mortality if not diagnosed early. Routine diagnostic methods, such as serum-based tests and biopsies, are either less effective in the early stages of the disease. This paper proposes an automatic detection of kidney disease using CNNs applied to medical imaging data. Our model is designed to analyze computed tomography (CT) images for the identification of kidney disease, classifying normal and tumors. The proposed CNN architecture leverages deep learning techniques to extract features from these images and classify them with high accuracy. This paper aims to build a system for detection of kidney disease using CNN, based on a public dataset sourced from Kaggle. The paper involves several key stages, initiated from raw data preprocessing and feature selection, followed by training and evaluating machine learning model using CNN. Our proposed model demonstrated superior performance in kidney disease detection, achieving an accuracy of 95%.
