COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest X-ray Images

Maya Pavlova; Naomi Terhljan; Audrey G Chung; Andy Zhao; Siddharth Surana; Hossein Aboutalebi; Hayden Gunraj; Ali Sabri; Amer Alaref; Alexander Wong

doi:10.3389/fmed.2022.861680

COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest X-ray Images

Front Med (Lausanne). 2022 Jun 10:9:861680. doi: 10.3389/fmed.2022.861680. eCollection 2022.

Authors

Maya Pavlova¹, Naomi Terhljan¹, Audrey G Chung^{2

3}, Andy Zhao¹, Siddharth Surana⁴, Hossein Aboutalebi^{2

4}, Hayden Gunraj¹, Ali Sabri^{5

6}, Amer Alaref^{7

8}, Alexander Wong^{1

2

3}

Affiliations

¹ Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.
² Waterloo AI Institute, University of Waterloo, Waterloo, ON, Canada.
³ DarwinAI Corp., Waterloo, ON, Canada.
⁴ Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.
⁵ Department of Radiology, McMaster University, Hamilton, ON, Canada.
⁶ Niagara Health System, St. Catharines, ON, Canada.
⁷ Department of Diagnostic Imaging, Northern Ontario School of Medicine, Thunder Bay, ON, Canada.
⁸ Department of Diagnostic Radiology, Thunder Bay Regional Health Sciences Centre, Thunder Bay, ON, Canada.

Abstract

As the COVID-19 pandemic devastates globally, the use of chest X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing continues to grow given its routine clinical use for respiratory complaint. As part of the COVID-Net open source initiative, we introduce COVID-Net CXR-2, an enhanced deep convolutional neural network design for COVID-19 detection from CXR images built using a greater quantity and diversity of patients than the original COVID-Net. We also introduce a new benchmark dataset composed of 19,203 CXR images from a multinational cohort of 16,656 patients from at least 51 countries, making it the largest, most diverse COVID-19 CXR dataset in open access form. The COVID-Net CXR-2 network achieves sensitivity and positive predictive value of 95.5 and 97.0%, respectively, and was audited in a transparent and responsible manner. Explainability-driven performance validation was used during auditing to gain deeper insights in its decision-making behavior and to ensure clinically relevant factors are leveraged for improving trust in its usage. Radiologist validation was also conducted, where select cases were reviewed and reported on by two board-certified radiologists with over 10 and 19 years of experience, respectively, and showed that the critical factors leveraged by COVID-Net CXR-2 are consistent with radiologist interpretations.

Keywords: COVID-19; chest X-ray; computer aided diagnosis; computer vision; deep neural networks.