Title:A Lightweight AMResNet Architecture with an Attention Mechanism for
Diagnosing COVID-19
Volume: 20
Author(s): Qi Zhou, Jamal Alzobair Hammad Kowah, Huijun Li, Mingqing Yuan, Lihe Jiang and Xu Liu*
Affiliation:
- Medical College, Guangxi University, Nanning 530004, P.R, China
Keywords:
Deep learning, COVID-19, Convolution neural network, ResNet, Chest X-ray images, Epidemic disease.
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.
