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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Aims

COVID-19 has become a worldwide epidemic disease and a new challenge for all mankind. The potential advantages of chest X-ray images on COVID-19 were discovered. We proposed a lightweight and effective Convolution Neural Network framework based on chest X-ray images for the diagnosis of COVID-19, named AMResNet.

Background

COVID-19 has become a worldwide epidemic disease and a new challenge for all mankind. The potential advantages of chest X-ray images on COVID-19 were discovered.

Objective

A lightweight and effective Convolution Neural Network framework based on chest X-ray images for the diagnosis of COVID-19.

Methods

By introducing the channel attention mechanism and image spatial information attention mechanism, a better level can be achieved without increasing the number of model parameters.

Results

In the collected data sets, we achieved an average accuracy rate of more than 92%, and the sensitivity and specificity of specific disease categories were also above 90%.

Conclusion

The convolution neural network framework can be used as a novel method for artificial intelligence to diagnose COVID-19 or other diseases based on medical images.

© 2023 Bentham Science Publishers
© 2023 The Author(s). Published by Bentham Science Publisher. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2024-01-01
2024-11-23

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/content/journals/cmir/10.2174/1573405620666230426121437
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A Lightweight AMResNet Architecture with an Attention Mechanism for Diagnosing COVID-19
Curr. Med. Imaging 20, e260423216195 (2024); https://doi.org/10.2174/1573405620666230426121437
/content/journals/cmir/10.2174/1573405620666230426121437
/content/journals/cmir/10.2174/1573405620666230426121437
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  • Article Type:     Research Article
Keyword(s): Chest X-ray images; Convolution neural network; COVID-19; Deep learning; Epidemic disease; ResNet

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