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

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dc.authoridAcharya, U Rajendra/0000-0003-2689-8552|Gundogan Bozdag, Pinar/0000-0002-7303-5832;
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.accessioned2025-10-24T18:08:59Z
dc.date.available2025-10-24T18:08:59Z
dc.date.issued2021
dc.departmentMalatya Turgut Özal Üniversitesi
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.
dc.identifier.doi10.1016/j.compbiomed.2021.104569
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid34157470
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2021.104569
dc.identifier.urihttps://hdl.handle.net/20.500.12899/3400
dc.identifier.volume135
dc.identifier.wosWOS:000687473200003
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofComputers In Biology And Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20251023
dc.subjectKidney stone; Medical image; Deep learning; Computed tomography
dc.titleDeep learning model for automated kidney stone detection using coronal CT images
dc.typeArticle

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