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COVID-19 detection from chest x-ray images using deep convolutional neural networks with weights imprinting approach

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.15353/jcvis.v6i1.3546
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Affiliation
  1. National Research Council Canada. Digital Technologies
FormatText, Article
ConferenceCVIS 2020 - Special Session on Computer Vision and Intelligent Systems at the 12th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020), March 23-26, 2020, Phuket, Thailand
Physical description3 p.
Abstract
COVID-19 pandemic has drastically changed our lives. Chest radiography has been used to detect COVID-19. However, the number of publicly available COVID-19 x-ray images is extremely limited, resulting in a highly imbalanced dataset. This is a challenge when using deep learning for classification and detection. In this work, we propose the use of pre-trained deep Convolutional Neural Networks (CNN) and integrate them with a few-shot learning approach named imprinted weights. The integrated model is fine tuned to enhance the capability of detecting COVID-19. The proposed solution then combines the fine-tuned models using a weighted average ensemble for achieving an optimal 82% sensitivity to COVID-19. To the best of authors’ knowledge, the proposed solution is one of the first to utilize imprinted weights model with weighted average ensemble for enhancing the model sensitivity to COVID-19.
Publication date
PublisherUniversity of Waterloo
In
  • Journal of Computational Vision and Imaging Systems 6, no. 1 (15 January 2021).
LanguageEnglish
Peer reviewedYes
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Record identifier85296412-4e4e-419b-9367-8b68ae8838f0
Record created2021-11-15
Record modified2021-11-15
Date modified: