  The world has suffered from COVID-19 (SARS-CoV-2) for the last two years,
causing much damage and change in people's daily lives. Thus, automated
detection of COVID-19 utilizing deep learning on chest computed tomography (CT)
scans became promising, which helps correct diagnosis efficiently. Recently,
transformer-based COVID-19 detection method on CT is proposed to utilize 3D
information in CT volume. However, its sampling method for selecting slices is
not optimal. To leverage rich 3D information in CT volume, we propose a
transformer-based COVID-19 detection using a novel data curation and adaptive
sampling method using gray level co-occurrence matrices (GLCM). To train the
model which consists of CNN layer, followed by transformer architecture, we
first executed data curation based on lung segmentation and utilized the
entropy of GLCM value of every slice in CT volumes to select important slices
for the prediction. The experimental results show that the proposed method
improve the detection performance with large margin without much difficult
modification to the model.
