Enhancing knee osteoarthritis detection with AI, image denoising, and optimized classification methods and the importance of physical therapy methods

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dc.contributor.authorBugday, Burak
dc.contributor.authorBingol, Harun
dc.contributor.authorYildirim, Muhammed
dc.contributor.authorAlatas, Bilal
dc.date.accessioned2026-04-04T13:30:40Z
dc.date.available2026-04-04T13:30:40Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractOsteoarthritis (OA) is considered one of the most challenging arthritic disorders due to its high disease burden and lack of effective treatment options that can change the course of the disease. Knee osteoarthritis (KOA) reduces people's quality of life and shortens their daily activities. Therefore, early detection of KOA dramatically impacts patients' quality of life. This study developed an artificial intelligence-supported system to detect KOA. In the developed system, firstly, the images in the original dataset were denoised with a Gaussian filter. Then, feature maps were extracted from both the original and Gaussian applied datasets with the DenseNet201 selected from eight different pre-trained models, and these two feature maps were concatenated. In this way, it is aimed to bring together different features of the same image. Then, feature selection was made using the neighborhood component analysis (NCA) method for the developed system to produce more successful results, and the optimized feature map was classified into six different classifiers. As a result, a high accuracy rate of 85% was achieved in the proposed model. This value is promising for the automatic diagnosis of KOA with computer-aided systems. As a result, a high accuracy rate of 85% was achieved in the developed system of the support vector machine (SVM) classifier. The proposed model was more successful than the other models used in the study.
dc.identifier.doi10.7717/peerj-cs.2766
dc.identifier.issn2376-5992
dc.identifier.orcid0000-0003-1866-4721
dc.identifier.orcid0000-0001-9806-291X
dc.identifier.orcid0000-0002-3513-0329
dc.identifier.pmid40567639
dc.identifier.scopus2-s2.0-105001841531
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.7717/peerj-cs.2766
dc.identifier.urihttps://hdl.handle.net/11616/108279
dc.identifier.volume11
dc.identifier.wosWOS:001480530400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPeerj Inc
dc.relation.ispartofPeerj Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectArtificial intelligence
dc.subjectCNN
dc.subjectGauss
dc.subjectKnee osteoarthritis
dc.subjectMachine learning
dc.titleEnhancing knee osteoarthritis detection with AI, image denoising, and optimized classification methods and the importance of physical therapy methods
dc.typeArticle

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