  This paper is responding to the MIA-COV19 challenge to classify COVID from
non-COVID based on CT lung images. The COVID-19 virus has devastated the world
in the last eighteen months by infecting more than 182 million people and
causing over 3.9 million deaths. The overarching aim is to predict the
diagnosis of the COVID-19 virus from chest radiographs, through the development
of explainable vision transformer deep learning techniques, leading to
population screening in a more rapid, accurate and transparent way. In this
competition, there are 5381 three-dimensional (3D) datasets in total, including
1552 for training, 374 for evaluation and 3455 for testing. While most of the
data volumes are in axial view, there are a number of subjects' data are in
coronal or sagittal views with 1 or 2 slices are in axial view. Hence, while 3D
data based classification is investigated, in this competition, 2D images
remains the main focus. Two deep learning methods are studied, which are vision
transformer (ViT) based on attention models and DenseNet that is built upon
conventional convolutional neural network (CNN). Initial evaluation results
based on validation datasets whereby the ground truth is known indicate that
ViT performs better than DenseNet with F1 scores being 0.76 and 0.72
respectively. Codes are available at GitHub at
<https://github/xiaohong1/COVID-ViT>.
