Kaya, DavutGurbuz, SukruYildirim, OkanAkbal, ErhanDogan, SengulTuncer, Turker2024-08-042024-08-0420231746-80941746-8108https://doi.org/10.1016/j.bspc.2023.105183https://hdl.handle.net/11616/101468Background 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.eninfo:eu-repo/semantics/closedAccessSwin architectureLung CT image classificationDirectional feature extractionTextural feature extractionFeature engineeringArticle8610.1016/j.bspc.2023.1051832-s2.0-85164703202Q1WOS:001147941300001Q1