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Automatic detection of spina bifida occulta with deep learning methods from plain pelvic radiographs

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dc.contributor.author Duran, Semra
dc.contributor.author Üreten, Kemal
dc.contributor.author Maraş, Yüksel
dc.contributor.author Maraş, Hadi Hakan
dc.contributor.author Gök, Kevser
dc.contributor.author Atalar, Ebru
dc.contributor.author Çayhan, Velihan
dc.date.accessioned 2023-11-28T12:58:18Z
dc.date.available 2023-11-28T12:58:18Z
dc.date.issued 2023-09
dc.identifier.citation Duran, Semra;...et.al. (2023). "Automatic detection of spina bifida occulta with deep learning methods from plain pelvic radiographs", Research on Biomedical Engineering, Vol.39, No.3, pp.655-661. tr_TR
dc.identifier.issn 24464732
dc.identifier.uri http://hdl.handle.net/20.500.12416/6664
dc.description.abstract Purpose: Spina bifida occulta (SBO), which is the most common congenital spinal deformity, is often seen in the lower lumbar spine and sacrum. In this study, it is aimed to develop a computer-aided diagnosis method that will help clinicians in the diagnosis of spina bifida occulta from plain pelvic radiographs with deep learning methods and transfer learning method. Materials and methods: The You Only Look Once (YOLO) algorithm was used for object detection, and classification was made by applying transfer learning with a pre-trained VGG-19, ResNet-101, MobileNetV2, and GoogLeNet networks. Our dataset consisted of 206 normal lumbosacral radiographs and 160 SBO lumbosacral radiographs. The performance of the models was evaluated by metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the ROC curve (AUC) results. Results: In the detection of SBO, 85.5%, 80.8%, 89.7%, 87.5%, 84%, and 0.92 accuracy, sensitivity, specificity, precision, F1 score, and AUC results were obtained with the pre-trained VGG-19 model, respectively. The pre-trained VGG-19 model performed better than the others. Conclusion: Successful results were obtained in this study performed to the diagnosis of SBO with deep learning methods. A model that will assist physicians in the diagnosis of SBO can be developed with new studies to be conducted with a large number of spinal radiographs. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1007/s42600-023-00296-6 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Deep Learning tr_TR
dc.subject Pre-Trained Models tr_TR
dc.subject Spina Bifida Occulta tr_TR
dc.subject Transfer Learning tr_TR
dc.subject YOLOv4 tr_TR
dc.title Automatic detection of spina bifida occulta with deep learning methods from plain pelvic radiographs tr_TR
dc.type article tr_TR
dc.relation.journal Research on Biomedical Engineering tr_TR
dc.contributor.authorID 34410 tr_TR
dc.identifier.volume 39 tr_TR
dc.identifier.issue 3 tr_TR
dc.identifier.startpage 655 tr_TR
dc.identifier.endpage 661 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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