Analysis of Ultrasound Images in Kidney Failure Diagnosis Using Deep Learning
Naresh Tiwari, Yazeed Yasin Ghadi, Marwan Omar
Abstract
Ultrasonic imaging has proven to be a valuable tool in kidney diagnosis, providing essential information about kidney size, shape, position, and function; and detecting structural abnormalities like cysts, stones, and infections. However, its effectiveness in kidney diagnostics is subject to operator expertise, leading to potential variations in image interpretation and diagnostic outcomes. It is crucial to explore automated approaches and computer-assisted diagnosis systems to address these challenges and enhance kidney diagnostics. Regrettably, the integration of such systems into kidney diagnostics has not been extensively investigated. Therefore, this study confirms the proposal of using a random forest classifier to detect kidney Nephrolithiasis. Notably, the classifier achieved an impressive accuracy of 96.33% compared to other machine learning classifiers, utilizing a test dataset of 100 kidney images.