COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models

TL;DR

This paper develops and evaluates vision transformer models for classifying COVID-19 from chest CT images, demonstrating improved accuracy over traditional CNNs in a competitive challenge setting.

Contribution

Introduces the application of vision transformer models to COVID-19 CT image classification, showing superior performance compared to DenseNet CNNs.

Findings

ViT achieved an F1 score of 0.76

ViT outperformed DenseNet with an F1 score of 0.72

Models enable rapid, accurate, and explainable COVID-19 diagnosis

Abstract

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>.