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Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods

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dc.contributor.author Maraş, Yüksel
dc.contributor.author Tokdemir, Gül
dc.contributor.author Üreten, Kemal
dc.contributor.author Atalar, Ebru
dc.contributor.author Duran, Semra
dc.contributor.author Maraş, Hakan
dc.date.accessioned 2024-03-05T13:00:27Z
dc.date.available 2024-03-05T13:00:27Z
dc.date.issued 2022
dc.identifier.citation Maraş, Yüksel;...et.al. (2022). "Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods", Joint Diseases and Related Surgery, Vol.33, No.1, pp.93-101. tr_TR
dc.identifier.issn 26874792
dc.identifier.uri http://hdl.handle.net/20.500.12416/7476
dc.description.abstract Cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology. Materials and methods: In this retrospective study, the convolutional neural networks were used and transfer learning method was applied with the pre-trained VGG-16, VGG-19, Resnet-101, and DenseNet-201 networks. Our data set consisted of 161 normal lateral cervical radiographs and 170 lateral cervical radiographs with osteoarthritis and cervical degenerative disc disease. Results: We compared the performances of the classification models in terms of performance metrics such as accuracy, sensitivity, specificity, and precision metrics. Pre-trained VGG-16 network outperformed other models in terms of accuracy (93.9%), sensitivity (95.8%), specificity (92.0%), and precision (92.0%) results. Conclusion: The results of this study suggest that the deep learning methods are promising support tool in automated control of cervical graphs using the DCNN and the exclusion of normal graphs. Such a supportive tool may reduce the diagnosis time and provide radiologists or clinicians to have more time to interpret abnormal graphs. © 2022. All right reserved by the Turkish Joint Diseases Foundation tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.52312/jdrs.2022.445 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Cervical Radiography tr_TR
dc.subject Convolutional Neural Network tr_TR
dc.subject Deep Learning tr_TR
dc.subject Disc Space Narrowing tr_TR
dc.subject Osteoarthritic Changes tr_TR
dc.subject Transfer Learning tr_TR
dc.title Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods tr_TR
dc.type article tr_TR
dc.relation.journal Joint Diseases and Related Surgery tr_TR
dc.contributor.authorID 17411 tr_TR
dc.contributor.authorID 34410 tr_TR
dc.identifier.volume 33 tr_TR
dc.identifier.issue 1 tr_TR
dc.identifier.startpage 93 tr_TR
dc.identifier.endpage 101 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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