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Articles

Vol. 6 No. 1 (2020): Special Issue: Proceedings of CVIS 2020

COVIDNet-CT: Detection of COVID-19 from Chest CT Images using a Tailored Deep Convolutional Neural Network Architecture

DOI
https://doi.org/10.15353/jcvis.v6i1.3547
Submitted
January 15, 2021
Published
2021-01-15

Abstract

The COVID-19 pandemic continues to have a tremendous impact on patients and healthcare systems around the world. To combat this disease, there is a need for effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as a key screening method to complement RT-PCR testing. Early studies have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, we introduce COVIDNet-CT, a deep convolutional neural network architecture tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a CT image dataset comprising 104,009 images across 1,489 patient cases. Finally, we leverage explainability to investigate the decision-making behaviour of COVIDNet-CT and ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images.