Abstract
The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients have worsening respiratory status or developing complications that require expedited care, or patients are suspected to be COVID-19-positive but have negative RT-PCR test results. Early studies on CT-based screening 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, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behavior of COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net initiative. While COVIDNet-CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.
Highlights
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to have a tremendous impact on patients and healthcare systems around the world
We quantitatively evaluate the performance of the proposed COVIDNet-computed tomography (CT) on the COVIDx-CT dataset
We introduced COVIDNet-CT, a deep convolutional neural network architecture tailored for detection of COVID-19 cases from chest CT images via machine-driven design exploration
Summary
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for fast and effective screening tools to identify patients infected with COVID-19 in order to ensure timely isolation and treatment. Reverse transcription polymerase chain reaction (RT-PCR) testing is the primary means of screening for COVID-19, as it can detect SARS-CoV2 ribonucleic acid (RNA) in sputum samples collected from the upper respiratory tract [1]. Chest computed tomography (CT) imaging has been proposed as an alternative screening tool for COVID-19 infection due to its high sensitivity, and may be effective when used as a complement to RT-PCR testing [4,5,6]. While cost and resource constraints limit routine CT screening for COVID-19 detection [7], CT imaging can be especially useful as a screening tool in situations where:
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