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. 2022 Mar 31;37(2):330–343. doi: 10.1007/s11390-020-0679-8

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

Xin Zhang 1,#, Siyuan Lu 2,#, Shui-Hua Wang 3,4,#, Xiang Yu 2,#, Su-Jing Wang 5,6, Lun Yao 7, Yi Pan 8, Yu-Dong Zhang 2,9,✉
PMCID: PMC9035772  PMID: 35496726

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.

Keywords: pneumonia, COVID-19, convolutional neural network, AlexNet, deep learning

Supplementary Information

ESM 1 (192.6KB, pdf)

(PDF 192 kb)

Footnotes

Xin Zhang, Siyuan Lu, Shui-Hua Wang and Xiang Yu contributed equally to this work.

Contributor Information

Xin Zhang, Email: hasyzx@njmu.edu.cn.

Siyuan Lu, Email: siyuan_lu@foxmail.com.

Shui-Hua Wang, Email: shuihuawang@ieee.org.

Xiang Yu, Email: xy144@le.ac.uk.

Su-Jing Wang, Email: wangsujing@psych.ac.cn.

Lun Yao, Email: jshayl@163.com.

Yi Pan, Email: yipan@gsu.edu.

Yu-Dong Zhang, Email: yudongzhang@ieee.org.

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Supplementary Materials

ESM 1 (192.6KB, pdf)

(PDF 192 kb)

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