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.
Original language | English |
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Article number | 2 |
Journal | ACM Transactions on Management Information Systems |
Volume | 13 |
Issue number | 1 |
DOIs | |
Publication status | Published - 5 Oct 2021 |
Externally published | Yes |
Keywords
- COVID-19
- Deep learning
- autoencoder
- fractional Fourier entropy