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

Frontiers in Medicine

article

Maya Pavlova¹  Naomi Terhljan¹  Audrey G. Chung²  Andy Zhao¹  Siddharth Surana⁴  Hossein Aboutalebi²  Hayden Gunraj¹  Ali Sabri⁵  Amer Alaref⁷  Alexander Wong¹ 

[1] Department of Systems Design Engineering, University of Waterloo;Waterloo AI Institute, University of Waterloo;DarwinAI Corp.;Cheriton School of Computer Science, University of Waterloo;Department of Radiology, McMaster University;Niagara Health System;Department of Diagnostic Imaging, Northern Ontario School of Medicine;Department of Diagnostic Radiology, Thunder Bay Regional Health Sciences Centre

关键词: COVID-19;    chest X-ray;    computer aided diagnosis;    computer vision;    deep neural networks;   

DOI  :  10.3389/fmed.2022.861680

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY