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

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dc.authoridyildirim, kadir/0000-0002-5380-2707
dc.authoridbozdag, pinar/0000-0002-7303-5832
dc.authoridAcharya, Rajendra U/0000-0003-2689-8552
dc.authorwosidyildirim, kadir/AAZ-2650-2021
dc.authorwosidbozdag, pinar/ADW-0920-2022
dc.authorwosidAcharya, Rajendra U/E-3791-2010
dc.contributor.authorYildirim, Kadir
dc.contributor.authorBozdag, Pinar Gundogan
dc.contributor.authorTalo, Muhammed
dc.contributor.authorYildirim, Ozal
dc.contributor.authorKarabatak, Murat
dc.contributor.authorAcharya, U. Rajendra
dc.date.accessioned2024-08-04T20:57:35Z
dc.date.available2024-08-04T20:57:35Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractKidney 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.en_US
dc.identifier.doi10.1016/j.compbiomed.2021.104569
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid34157470en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2021.104569
dc.identifier.urihttps://hdl.handle.net/11616/102753
dc.identifier.volume135en_US
dc.identifier.wosWOS:000687473200003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectKidney stoneen_US
dc.subjectMedical imageen_US
dc.subjectDeep learningen_US
dc.subjectComputed tomographyen_US
dc.titleDeep learning model for automated kidney stone detection using coronal CT imagesen_US
dc.typeArticleen_US

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