Publication

Abstract : Nephrolithiasis is the medical term used to define the urological disorders that occur in human beings. Kidney disorders are an area of critical concern, so early diagnosis and treatment are vital in saving the patient’s life. The variety and complexity of kidney problems make it very challenging to diagnose with accuracy. Most such disorders occur without any symptoms, which makes them much more difficult to detect at an earlier stage. In order to save the patient’s life, early identification and therapy are essential. This research aims to create a diagnostic system that can precisely and accurately classify kidney computed tomography scan images into four categories: stones, cysts, tumors, and normal states. The traditional methods of identification are very challenging and time-consuming, especially using CT scan images. In this study, we developed a customized Convolutional Neural Network model that accurately classifies the CT scan images into different categories. This research study introduces an innovative approach that addresses the diversity and complexities of nephrolithiasis. The customized CNN model used in this research gives an accuracy of 99.2%, a precision of 98.9%, and a recall of 98.6%, proving it is very efficient and highly reliable for classifying medical data.