A Novel Deep Hybrid Model for Automatic Femoral Stem Classification in Hip Arthroplasty From Radiographs: MSFT-Net With CBAM and Transformer Modules

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dc.authorid0000-0003-0038-7519
dc.authorid0000-0003-3263-9290
dc.authorid0009-0008-5200-5301
dc.contributor.authorGogus, Emre
dc.contributor.authorYilmaz, Atinc
dc.contributor.authorEnercan, Meric
dc.date.accessioned2026-01-31T15:08:39Z
dc.date.available2026-01-31T15:08:39Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractAccurate identification of femoral stem implants in hip arthroplasty is essential for effective revision surgery, minimizing operative complexity, patient morbidity, intraoperative blood loss, and postoperative recovery time. In cases where prior implant data are unavailable, manual identification is often required, posing significant challenges due to its time-consuming and error-prone nature. To solve this problem, a novel hybrid deep learning architecture that includes a convolutional block attention module and a swin transformer with multi-scale feature fusion from pre-trained architectures DenseNet201, VGG19, and InceptionV3 under the transfer learning paradigm was proposed in this study. The proposed multi-scale feature transformer network was trained and validated on a dataset comprising 1266 anteroposterior (A.P.) hip radiographs of 10 different femoral stem implant types. The proposed hybrid deep learning architecture achieved a training accuracy of 96.7% and validation accuracy of 94.86%, significantly outperforming other baseline models. Compared with state-of-the-art methods, the proposed model achieved an absolute accuracy improvement of 9.5% over VGG19 and 7.4% over DenseNet201 and 8.8% over InceptionV3, demonstrating a significant advancement over existing models in femoral stem classification. The average inference time per image was under 1 second. The experimental results demonstrated that the proposed architecture enhances classification performance while reducing overfitting through attention and transformer-based feature refinement. This automated approach facilitates real-time preoperative implant recognition, thereby streamlining surgical planning, potentially reducing operative costs and duration, and improving clinical outcomes.
dc.identifier.doi10.1109/ACCESS.2025.3578919
dc.identifier.endpage102577
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-105007938416
dc.identifier.scopusqualityQ1
dc.identifier.startpage102564
dc.identifier.urihttps://doi.org./10.1109/ACCESS.2025.3578919
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10717
dc.identifier.volume13
dc.identifier.wosWOS:001511065700010
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260128
dc.subjectImplants
dc.subjectRadiography
dc.subjectHip
dc.subjectSurgery
dc.subjectAccuracy
dc.subjectTransformers
dc.subjectX-ray imaging
dc.subjectComputer architecture
dc.subjectConvolutional neural networks
dc.subjectTraining
dc.subjectCBAM attention mechanism
dc.subjectdeep learning
dc.subjectfemoral stem classification
dc.subjecthip implant
dc.subjecthip arthroplasty
dc.subjectmulti-scale feature fusion
dc.subjectorthopedic surgery
dc.subjectswin transformer
dc.titleA Novel Deep Hybrid Model for Automatic Femoral Stem Classification in Hip Arthroplasty From Radiographs: MSFT-Net With CBAM and Transformer Modules
dc.typeArticle

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