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Detection of hand osteoarthritis from hand radiographs using convolutional neural networks with transfer learning

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dc.contributor.author Üreten, Kemal
dc.contributor.author Erbay, Hasan
dc.contributor.author Maraş, Hadi Hakan
dc.date.accessioned 2022-04-01T12:13:22Z
dc.date.available 2022-04-01T12:13:22Z
dc.date.issued 2020
dc.identifier.citation Üreten, Kemal; Erbay, Hasan; Maraş, Hadi Hakan (2020). "Detection of hand osteoarthritis from hand radiographs using convolutional neural networks with transfer learning", Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 28, No. 5, pp. 2968-2978. tr_TR
dc.identifier.issn 1303-6203
dc.identifier.uri http://hdl.handle.net/20.500.12416/5246
dc.description.abstract Osteoarthritis is the most common type of arthritis. Hand osteoarthritis leads to specific structural changes in the joints, such as asymmetric joint space narrowing and osteophytes (bone spurs). Conventional radiography has traditionally been the primary method of visualizing these structural changes and diagnosing osteoarthritis. We aimed to develop a computerized method that is capable of determining the structural changes seen in radiography of the hand and to assist practitioners in interpreting radiographic changes and diagnosing the disease. In this retrospective study, transfer-learning-based convolutional neural networks were trained on a randomly selected dataset containing 332 radiography images of hands from an original set of 420 and were validated with the remaining 88. Multilayer convolutional neural network models were designed based on a transfer learning method using pretrained AlexNet, GoogLeNet, and VGG-19 networks. The accuracies of the models were 93.2% for AlexNet, 94.3% for GoogLeNet, and 96.6% for VGG-19. The sensitivities of these models were 0.9167 for AlexNet, 0.9184 for GoogLeNet, and 0.9574 for VGG-19, while the specificity values were 0.9500, 0.9744, and 0.9756, respectively. The performance metrics, including accuracy, sensitivity, specificity, and precision, of our newly developed automated diagnosis methods are promising in the diagnosis of hand osteoarthritis. Our computer-aided detection systems may help physicians in interpreting hand radiography images, diagnosing osteoarthritis, and saving time. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.3906/elk-1912-23 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Hand Osteoarthritis tr_TR
dc.subject Convolutional Neural Networks tr_TR
dc.subject Transfer Learning tr_TR
dc.subject Conventional Hand Radiography tr_TR
dc.subject Classification tr_TR
dc.title Detection of hand osteoarthritis from hand radiographs using convolutional neural networks with transfer learning tr_TR
dc.type article tr_TR
dc.relation.journal Turkish Journal of Electrical Engineering and Computer Sciences tr_TR
dc.contributor.authorID 34410 tr_TR
dc.identifier.volume 28 tr_TR
dc.identifier.issue 5 tr_TR
dc.identifier.startpage 2968 tr_TR
dc.identifier.endpage 2978 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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