Deep learning model for automated kidney stone detection using coronal CT images. (August 2021)

• Record Type: Journal Article
• Authors: Yildirim, Kadir; Bozdag, Pinar Gundogan; Talo, Muhammed; Yildirim, Ozal; Karabatak, Murat; Acharya, U.Rajendra
• Is Part Of: Computers in biology and medicine. Volume 135(2021)
• Publication Date: 2021-08
• Subjects: Kidney stone -- Medical image -- Deep learning -- Computed tomography; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104569
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Kidney stones are a common complaint worldwide, causing many people to admit to emergency rooms with severe pain. Various imaging techniques are used for the diagnosis of kidney stone disease. Specialists are needed for the interpretation and full diagnosis of these images. Computer-aided diagnosis systems are the practical approaches that can be used as auxiliary tools to assist the clinicians in their diagnosis. In this study, an automated detection of kidney stone (having stone/not) using coronal computed tomography (CT) images is proposed with deep learning (DL) technique which has recently made significant progress in the field of artificial intelligence. A total of 1799 images were used by taking different cross-sectional CT images for each person. Our developed automated model showed an accuracy of 96.82% using CT images in detecting the kidney stones. We have observed that our model is able to detect accurately the kidney stones of even small size. Our developed DL model yielded superior results with a larger dataset of 433 subjects and is ready for clinical application. This study shows that recently popular DL methods can be employed to address other challenging problems in urology. Highlights: A deep model is proposed for the kidney stone detection using CT images. A total of 1799 images were used by taking different cross-sectional CT images for each person. The proposed DL model yielded 96.82% accuracy rate and 95.76% sensitivity on the 146 test cases. The proposed model can identify correct regions of interest on the images for accurate decision making.