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In here, we introduce a novel approach to enhance the accuracy and efficiencyof COVID-19 diagnosis using CT images. Leveraging state-of-the-art Transformermodels in computer vision, we employed the base ViT Transformer configured for224x224-sized input images, modifying the output to suit the binaryclassification task. Notably, input images were resized from the standard CTscan size of 512x512 to match the model's expectations. Our method implements asystematic patient-level prediction strategy, classifying individual CT slicesas COVID-19 or non-COVID. To determine the overall diagnosis for each patient,a majority voting approach as well as other thresholding approaches wereemployed. This method involves evaluating all CT slices for a given patient andassigning the patient the diagnosis that relates to the thresholding for the CTscan. This meticulous patient-level prediction process contributes to therobustness of our solution as it starts from 2D-slices to 3D-patient level.Throughout the evaluation process, our approach resulted in 0.7 macro F1 scoreon the COV19-CT -DB validation set. To ensure the reliability and effectivenessof our model, we rigorously validate it on the extensive COV-19 CT dataset,which is meticulously annotated for the task. This dataset, with itscomprehensive annotations, reinforces the overall robustness of our solution.

COVID-19 detection using ViT transformer-based approach from Computed Tomography Images