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

• Published in: Frontiers in medicine 2022-06, Vol.9, p.861680-861680
• Main Authors: Pavlova, Maya, Terhljan, Naomi, Chung, Audrey G., Zhao, Andy, Surana, Siddharth, Aboutalebi, Hossein, Gunraj, Hayden, Sabri, Ali, Alaref, Amer, Wong, Alexander
• Format: Article
• Language: English
• Subjects: chest X-ray; computer aided diagnosis; computer vision; COVID-19; deep neural networks; Medicine
• ISSN: 2296-858X; 2296-858X
• DOI: 10.3389/fmed.2022.861680

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.