Swin-PHOG-LPQ: An accurate computed tomography images classification model using Swin architecture with handcrafted features

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dc.authoridDOGAN, Sengul/0000-0001-9677-5684;
dc.authorwosidDOGAN, Sengul/W-4854-2018
dc.authorwosidAKBAL, Erhan/W-4823-2018
dc.contributor.authorKaya, Davut
dc.contributor.authorGurbuz, Sukru
dc.contributor.authorYildirim, Okan
dc.contributor.authorAkbal, Erhan
dc.contributor.authorDogan, Sengul
dc.contributor.authorTuncer, Turker
dc.date.accessioned2024-08-04T20:54:31Z
dc.date.available2024-08-04T20:54:31Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground and aim: Computed tomography (CT) image classification has been the subject of intense research in the area of biomedical image classification with the objective of developing intelligent disorder detection models. In this paper, we aim to detect three disorders in lung CT images: hemothorax, contusion, and pneumothorax. Deep learning models are particularly effective for computer vision tasks. Thus our second goal is to propose a new hand-modeled image classification model that achieves high performance using the shifted windows (swin) architecture.Materials and Methods: We collected a new lung CT image dataset containing four classes - hemothorax, contusion, pneumothorax, and control - with 2730 CT images. Our proposed swin architecture-based CT image classification model is designed to extract features from patches using the Pyramidal histogram-oriented gradient (PHOG) and local phase quantization (LPQ) methods for directional and textural features. We utilized an iterative neighborhood component analysis (INCA) feature selector for feature selection and classified the chosen features using the k-nearest neighbors (kNN) classifier with 10-fold cross-validation. Finally, majority voting was employed to obtain the final classification.Results: Our proposed Swin-PHOG-LPQ achieved a classification accuracy of 95.53%. We also evaluated our model on two publicly available CT image datasets and achieved classification accuracies of 95.31% and 97.63%, respectively.Conclusion: The high classification accuracies obtained by our proposed Swin-PHOG-LPQ model demonstrate its efficacy in detecting the three disorders in lung CT images.en_US
dc.identifier.doi10.1016/j.bspc.2023.105183
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85164703202en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2023.105183
dc.identifier.urihttps://hdl.handle.net/11616/101468
dc.identifier.volume86en_US
dc.identifier.wosWOS:001147941300001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSwin architectureen_US
dc.subjectLung CT image classificationen_US
dc.subjectDirectional feature extractionen_US
dc.subjectTextural feature extractionen_US
dc.subjectFeature engineeringen_US
dc.titleSwin-PHOG-LPQ: An accurate computed tomography images classification model using Swin architecture with handcrafted featuresen_US
dc.typeArticleen_US

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