Image Based Chronic Renal Disease Diagnosis Using Convolution Neural Network Deep Learning Approach

Abstract

Kidney disease presents a major health challenge, with conditions like kidney stones requiring timely diagnosis and treatment. Traditional radiological methods are time-consuming and dependent on expert interpretation. This study proposes an automated kidney stone detection system using deep learning techniques, employing CNN for classification. A diverse dataset, including normal kidney tissue, kidney stones, cysts, and tumors, is utilized. The methodology includes data preprocessing, feature selection, and CNN-based classification, achieving a high accuracy of 88.43%.

Additionally, this research explores Chronic Kidney Disease (CKD) prediction using a modified CNN architecture, incorporating data augmentation and preprocessing for enhanced accuracy. The model is evaluated against existing machine learning approaches, and contour plotting is employed to assess severity levels.Results highlight the potential of deep learning in improving early detection, reducing diagnostic time, and assisting radiologists in kidney disease management.