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dc.contributor.authorSrinivasan, Saravanan
dc.contributor.authorGunasekaran, Subathra
dc.contributor.authorMathivanan, Sandeep Kumar
dc.contributor.authorJayagopal, Prabhu
dc.contributor.authorKhan, Muhammad Attique
dc.contributor.authorAlasiry, Areej
dc.contributor.authorMarzougui, Mehrez
dc.contributor.authorMasood, Anum
dc.date.accessioned2023-11-17T15:35:59Z
dc.date.available2023-11-17T15:35:59Z
dc.date.created2023-05-22T13:17:19Z
dc.date.issued2023
dc.identifier.citationDiagnostics (Basel). 2023, 13 (8), .en_US
dc.identifier.issn2075-4418
dc.identifier.urihttps://hdl.handle.net/11250/3103325
dc.description.abstractWe developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior–posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RAen_US
dc.title.alternativeA Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RAen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber0en_US
dc.source.volume13en_US
dc.source.journalDiagnostics (Basel)en_US
dc.source.issue8en_US
dc.identifier.doi10.3390/diagnostics13081385
dc.identifier.cristin2148485
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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