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Current Medical Imaging

Author(s): Qi Zhou, Jamal Alzobair Hammad Kowah, Huijun Li, Mingqing Yuan, Lihe Jiang and Xu Liu*

DOI: 10.2174/1573405620666230426121437

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A Lightweight AMResNet Architecture with an Attention Mechanism for Diagnosing COVID-19

Article ID: e260423216195 Pages: 8

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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.

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