Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture.
J Comput Sci Technol
; 37(2): 330-343, 2022.
Article
in English
| MEDLINE | ID: covidwho-1803050
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Diagnostic study
Language:
English
Journal:
J Comput Sci Technol
Year:
2022
Document Type:
Article
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