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Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods

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dc.contributor.author Üreten, Kemal
dc.contributor.author Maraş, Hadi Hakan
dc.date.accessioned 2024-02-05T12:49:12Z
dc.date.available 2024-02-05T12:49:12Z
dc.date.issued 2022-04
dc.identifier.citation Üreten, K.; Maraş, H.H. (2022). "Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods", Journal of Digital Imaging, Vol.35, No.2, pp.193-199. tr_TR
dc.identifier.issn 08971889
dc.identifier.uri http://hdl.handle.net/20.500.12416/7072
dc.description.abstract Rheumatoid arthritis and hand osteoarthritis are two different arthritis that causes pain, function limitation, and permanent joint damage in the hands. Plain hand radiographs are the most commonly used imaging methods for the diagnosis, differential diagnosis, and monitoring of rheumatoid arthritis and osteoarthritis. In this retrospective study, the You Only Look Once (YOLO) algorithm was used to obtain hand images from original radiographs without data loss, and classification was made by applying transfer learning with a pre-trained VGG-16 network. The data augmentation method was applied during training. The results of the study were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated from the confusion matrix, and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. In the classification of rheumatoid arthritis and normal hand radiographs, 90.7%, 92.6%, 88.7%, 89.3%, and 0.97 accuracy, sensitivity, specificity, precision, and AUC results, respectively, and in the classification of osteoarthritis and normal hand radiographs, 90.8%, 91.4%, 90.2%, 91.4%, and 0.96 accuracy, sensitivity, specificity, precision, and AUC results were obtained, respectively. In the classification of rheumatoid arthritis, osteoarthritis, and normal hand radiographs, an 80.6% accuracy result was obtained. In this study, to develop an end-to-end computerized method, the YOLOv4 algorithm was used for object detection, and a pre-trained VGG-16 network was used for the classification of hand radiographs. This computer-aided diagnosis method can assist clinicians in interpreting hand radiographs, especially in rheumatoid arthritis and osteoarthritis. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1007/s10278-021-00564-w tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Data Augmentation tr_TR
dc.subject Deep Learning tr_TR
dc.subject Object Detection tr_TR
dc.subject Osteoarthritis tr_TR
dc.subject Rheumatoid Arthritis tr_TR
dc.subject Transfer Learning tr_TR
dc.title Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods tr_TR
dc.type article tr_TR
dc.relation.journal Journal of Digital Imaging tr_TR
dc.contributor.authorID 34410 tr_TR
dc.identifier.volume 35 tr_TR
dc.identifier.issue 2 tr_TR
dc.identifier.startpage 193 tr_TR
dc.identifier.endpage 199 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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