Deep learning model for automated kidney stone detection using coronal CT images

被引:72
作者
Yildirim, Kadir [1 ]
Bozdag, Pinar Gundogan [2 ]
Talo, Muhammed [3 ]
Yildirim, Ozal [3 ]
Karabatak, Murat [3 ]
Acharya, U. Rajendra [4 ,5 ,6 ]
机构
[1] Univ Turgut Ozal, Fac Med, Dept Urol, Malatya, Turkey
[2] Elazig Fethi Sekin City Hosp, Dept Radiol, Elazig, Turkey
[3] Firat Univ, Dept Software Engn, Elazig, Turkey
[4] Ngee Ann Polytech, Dept Elect & Comp Engn, Singapore, Singapore
[5] Asia Univ, Dept Bioinformat & Med Engn, Taichung, Taiwan
[6] Univ Southern Queensland, Sch Management & Enterprise, Springfield, Australia
关键词
Kidney stone; Medical image; Deep learning; Computed tomography; CLASSIFICATION; ERROR;
D O I
10.1016/j.compbiomed.2021.104569
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:7
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