Deep learning model for automated kidney stone detection using coronal CT images
Article
Yildirim, Kadir, Bozdag, Pinar Gundogan, Talo, Muhammed, Yildirim, Ozal, Karabatak, Murat and Acharya, U. Rajendra. 2021. "Deep learning model for automated kidney stone detection using coronal CT images." Computers in Biology and Medicine. 135. https://doi.org/10.1016/j.compbiomed.2021.104569
Article Title | Deep learning model for automated kidney stone detection using coronal CT images |
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ERA Journal ID | 5040 |
Article Category | Article |
Authors | Yildirim, Kadir, Bozdag, Pinar Gundogan, Talo, Muhammed, Yildirim, Ozal, Karabatak, Murat and Acharya, U. Rajendra |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 135 |
Article Number | 104569 |
Number of Pages | 7 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0010-4825 |
1879-0534 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2021.104569 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0010482521003632 |
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. |
Keywords | Computed tomography; Kidney stone; Deep learning; Medical image |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Turgut Ozal, Turkey |
Elazig Fethi Sekin City Hospital, Turkiye | |
Firat University, Turkey | |
School of Mathematics, Physics and Computing | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan |
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