Automated Kidney Disease Detection Using Machine Learning and Computer Vision Basedon Radiology Image Analysis

This article provides a detailed method for detecting kidney disease using both images from radiology and analyzing data from urine samples. A Convolutional Neural Network (CNN) was used to categorize the radiology images into four classes; normal, cyst, tumor, and stone. The CNN model was effective with an impressive validation accuracy of 92%, differentiating the different kidney conditions with efficiency. In addition, urine analysis data was utilized to predict the chances of having kidney diseases using conventional machine learning models such as Support Vector Machines (SVM) and Random Forest. Where the Random Forest model scored an 87% accuracy, 85% precision, 88% recall, and 86% f1-score, outperforming the SVM model. Hence the findings indicate that it is possible to achieve early diagnosis of kidney-associated disorders through a mix of imaging and non-imaging modalities. The system shows great potential in enhancing diagnostic accuracy and may be widely adopted in the healthcare setting. From this point on, another direction of improvement would be the implementation of even more complex deep learning models and segmentation techniques to increase the performance of the system.