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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture

Authors
  • Zhang, Xin
  • Lu, Siyuan
  • Wang, Shui-Hua
  • Yu, Xiang
  • Wang, Su-Jing
  • Yao, Lun
  • Pan, Yi
  • Zhang, Yu-Dong
Type
Published Article
Journal
Journal of Computer Science and Technology
Publisher
Springer-Verlag
Publication Date
Mar 31, 2022
Volume
37
Issue
2
Pages
330–343
Identifiers
DOI: 10.1007/s11390-020-0679-8
PMID: 35496726
PMCID: PMC9035772
Source
PubMed Central
Keywords
Disciplines
  • Regular Paper
License
Unknown

Abstract

COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network AlexNet as the backbone, and enhance it with the following two aspects: 1) adding batch normalization to help accelerate the training, reducing the internal covariance shift; 2) replacing the fully connected layer in AlexNet with three classifiers: SNN, ELM, and RVFL. Therefore, we have three novel models from the deep COVID network (DC-Net) framework, which are named DC-Net-S, DC-Net-E, and DC-Net-R, respectively. After comparison, we find the proposed DC-Net-R achieves an average accuracy of 90.91% on a private dataset (available upon email request) comprising of 296 images while the specificity reaches 96.13%, and has the best performance among all three proposed classifiers. In addition, we show that our DC-Net-R also performs much better than other existing algorithms in the literature. Supplementary Information The online version contains supplementary material available at 10.1007/s11390-020-0679-8.

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