COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

Abstract

The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.

Figure 1

Example CXR images of: (A) non-COVID19 infection, and (B) COVID-19 viral infection in the COVIDx dataset.

Figure 2

Example CXR images from the COVIDx dataset, which comprises of 13,975 CXR images across 13,870 patient cases from five open access data repositories: (1) COVID-19 Image Data Collection, (2) Figure 1 COVID-19 Chest X-Ray Dataset Initiative (established with Fig. 1), (3) RSNA Pneumonia Detection challenge dataset, (4) ActualMed COVID-19 Chest X-Ray Dataset Initiative (established with ActualMed), and (5) COVID-19 radiography database.

Figure 3

CXR images distribution for each infection type of the COVIDx dataset (normal means no infection). (Left bar) number of training images, (right bar) number of test images.

Figure 4

Number of patient cases for each infection type of the COVIDx dataset (normal means no infection). (Left bar) number of patient cases for training, (right bar) number of patient cases for testing.

Figure 5

COVID-Net architecture. High architectural diversity and selective long-range connectivity can be observed as it is tailored for COVID-19 case detection from CXR images. The heavy use of a projection-expansion-projection design pattern in the COVID-Net architecture can also be observed, which provides enhanced representational capacity while maintaining computational efficiency.

Figure 6

Confusion matrix for COVID-Net on the COVIDx test dataset.

Figure 7

Example CXR images of COVID-19 cases from several different patients and their associated critical factors (highlighted in red) as identified by GSInquire.