Background 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 reflux 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.
Keywords: Children; Classifiers; Deep learning; Vesicoureteral Reflux; Voiding cystourethrography; mRMR.
Copyright © 2021. Published by Elsevier B.V.