Diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid model

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dc.authoridyildirim, kadir/0000-0002-5380-2707
dc.authoridYILDIRIM, Muhammed/0000-0003-1866-4721;
dc.authorwosidyildirim, kadir/AAZ-2650-2021
dc.authorwosidYILDIRIM, Muhammed/AAK-6342-2021
dc.authorwosidCINAR, Ahmet/W-5792-2018
dc.contributor.authorEroglu, Yesim
dc.contributor.authorYildirim, Kadir
dc.contributor.authorCinar, Ahmet
dc.contributor.authorYildirim, Muhammed
dc.date.accessioned2024-08-04T20:57:41Z
dc.date.available2024-08-04T20:57:41Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground and objective: Vesicoureteral reflux is the leakage of urine from the bladder into the ureter. As a result, urinary tract infections and kidney scarring can occur in children. Voiding cystourethrography is the primary radiological imaging method used to diagnose vesicoureteral reflux in children with a history of recurrent urinary tract infection. Besides the diagnosis of reflux, it is graded with voiding cystourethrography. In this study, we aimed to diagnose and grade vesicoureteral reflux in Voiding cystourethrography images using hybrid CNN in deep learning methods. Methods: Images of pediatric patients diagnosed with VUR between 2016 and 2021 in our hospital (Firat University Hospital) were graded according to the international vesicoureteral reflux radiographic grading system. VCUG images of 236 normal and 992 with vesicoureteral reflux pediatric patients were available. A total of 6 classes were created as normal and graded 1-5 patients. Results: In this study, a hybrid-based mRMR (Minimum Redundancy Maximum Relevance) using CNN (Convolutional Neural Networks) model is developed for the diagnosis and grading of vesicoureteral re flux on voiding cystourethrography images. Googlenet, MobilenetV2, and Densenet201 models are used as a part of the hybrid architecture. The obtained features from these architectures are examined in concatenating process. Then, these features are classified in machine learning classifiers after optimizing with the mRMR method. Among the models used in the study, the highest accuracy value was obtained in the proposed model with an accuracy rate of 96.9%. Conclusions: It shows that the hybrid model developed according to the findings of our study can be used in the diagnosis and grading of vesicoureteral reflux in voiding cystourethrography images. (c) 2021 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2021.106369
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.pmid34474195en_US
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2021.106369
dc.identifier.urihttps://hdl.handle.net/11616/102830
dc.identifier.volume210en_US
dc.identifier.wosWOS:000718164200018en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods and Programs in Biomedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectVoiding cystourethrographyen_US
dc.subjectDeep learningen_US
dc.subjectmRMRen_US
dc.subjectVesicoureteral Refluxen_US
dc.subjectClassifiersen_US
dc.subjectChildrenen_US
dc.titleDiagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid modelen_US
dc.typeArticleen_US

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