Affiliation:
1. University of Leicester, Leicester, UK
2. Fourth People's Hospital of Huai'an, Huai'an, Jiangsu Province, China
Abstract
(
Aim
) COVID-19 has caused more than 2.28 million deaths till 4/Feb/2021 while it is still spreading across the world. This study proposed a novel artificial intelligence model to diagnose COVID-19 based on chest CT images. (
Methods
) First, the two-dimensional fractional Fourier entropy was used to extract features. Second, a custom
deep stacked sparse autoencoder (DSSAE)
model was created to serve as the classifier. Third, an improved multiple-way data augmentation was proposed to resist overfitting. (
Results
) Our DSSAE model obtains a micro-averaged F1 score of 92.32% in handling a four-class problem (COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy control). (
Conclusion
) Our method outperforms 10 state-of-the-art approaches.
Funder
Royal Society International Exchanges Cost Share Award, UK
Medical Research Council Confidence in Concept Award, UK
Hope Foundation for Cancer Research, UK
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Management Information Systems
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