Automatic detection of knee osteoarthritis grading using artificial intelligence-based methods

Basit Öğe Kaydı
dc.contributor.authorYildirim, Muhammed
dc.contributor.authorMutlu, Hursit Burak
dc.date.accessioned2026-06-19T06:41:18Z
dc.date.available2026-06-19T06:41:18Z
dc.date.issued2024
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractOsteoarthritis (OA) means that the slippery cartilage tissue that covers the bone surfaces in the joints and allows the joint to move easily loses its properties and wears out. Knee OA is the wear and tear of the cartilage in the knee joint. Knee OA is a disease whose incidence increases especially after a certain age. Knee OA is difficult and costly to be detected by specialists using traditional methods and may lead to misdiagnosis. In this study, computer-aided systems were used to prevent errors in traditional methods of detecting knee OA, shorten the diagnosis time, and accelerate the treatment process. In this study, a hybrid model was developed by using Darknet53, Histogram of Directional Gradients (HOG), Local Binary Model (LBP) methods for feature extraction, and Neighborhood Component Analysis (NCA) for feature selection. Our dataset used in experiments contains 1650 knee joint images and consists of five classes: Normal, Doubtful, Mild, Moderate, and Severe. In the experimental studies performed, the performance of the proposed method was compared with eight different Convolutional Neural Networks (CNN) Models. The developed model achieved better performance metrics than the eight different models used in the study and similar studies in the literature. The accuracy value of the developed model is 83.6%.
dc.identifier.doi10.1002/ima.23057
dc.identifier.issn0899-9457
dc.identifier.issn1098-1098
dc.identifier.issue2
dc.identifier.orcid0009-0009-2176-0192
dc.identifier.scopus2-s2.0-85188248707
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/ima.23057
dc.identifier.urihttps://hdl.handle.net/20.500.12899/6179
dc.identifier.volume34
dc.identifier.wosWOS:001187162500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofInternational Journal of Imaging Systems and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectCnn
dc.subjectDeep Learning
dc.subjectHog
dc.subjectLbp
dc.subjectNca
dc.titleAutomatic detection of knee osteoarthritis grading using artificial intelligence-based methods
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu