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

• URL: http://arxiv.org/abs/2105.06640v1
• Date: Fri, 14 May 2021 04:29:21 GMT
• Title: COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images
• Authors: Maya Pavlova, Naomi Terhljan, Audrey G. Chung, Andy Zhao, Siddharth Surana, Hossein Aboutalebi, Hayden Gunraj, Ali Sabri, Amer Alaref, and Alexander Wong
• Abstract summary: Use of chest X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing continues to grow. We introduce COVID-Net CXR-2, an enhanced deep convolutional neural network design for COVID-19 detection from CXR images. benchmark dataset composed of 19,203 CXR images from a multinational cohort of 16,656 patients from at least 51 countries.
• Score: 58.35627258364233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the COVID-19 pandemic continues to devastate 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. To facilitate this, 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%/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 behaviour 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. While not a production-ready solution, we hope the open-source, open-access release of COVID-Net CXR-2 and the respective CXR benchmark dataset will encourage researchers, clinical scientists, and citizen scientists to accelerate advancements and innovations in the fight against the pandemic.

Related papers

• COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics [69.55060769611916]
  The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is increasing. Many visual perception models have been proposed for COVID-19 screening based on CXR imaging. We introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research.
  arXiv  Detail & Related papers  (2022-06-08T04:39:44Z)
• Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays [7.509537244787675]
  We propose the evaluation of convolutional neural networks to identify pneumonia due to COVID-19 in Digital Chest X-rays. The proposed methodology consists of a preprocessing step of the XR, data augmentation, and classification by the convolutional architectures DenseNet121, InResceptionNetV2, InceptionV3, MovileNetV2, ResNet50, and VGG16. The obtained results demonstrate that the VGG16 architecture presented a superior performance in the classification of XR.
  arXiv  Detail & Related papers  (2021-09-06T12:57:55Z)
• COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images [74.77272804752306]
  We introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient's chest. We leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 patient cases into a custom network architecture for severity assessment. The proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients.
  arXiv  Detail & Related papers  (2021-05-01T14:15:12Z)
• COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning [70.92379567261304]
  We introduce COVID-Net CT-2, enhanced deep neural networks for COVID-19 detection from chest CT images. We leverage explainability to investigate the decision-making behaviour of COVID-Net CT-2. Results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment.
  arXiv  Detail & Related papers  (2021-01-19T03:04:09Z)
• COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity [58.23203766439791]
  Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity. In this study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system.
  arXiv  Detail & Related papers  (2020-05-26T16:33:52Z)
• COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images [93.0013343535411]
  We introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images. 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. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases.
  arXiv  Detail & Related papers  (2020-03-22T12:26:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.