Journal of Computer Science and Technology ›› 2022, Vol. 37 ›› Issue (2): 330-343.doi: 10.1007/s11390-020-0679-8

Special Issue: Artificial Intelligence and Pattern Recognition

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

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

Xin Zhang1 (张鑫), Siyuan Lu2 (陆思源), Shui-Hua Wang3,4 (王水花), Xiang Yu2 (余翔), Su-Jing Wang5,6 (王甦菁), Lun Yao7 (姚仑), Yi Pan8 (潘毅), and Yu-Dong Zhang2,9,* (张煜东)        

  1. 1Department of Medical Imaging, The Fourth People's Hospital of Huai'an, Huai'an 223002, China
    2School of Informatics, University of Leicester, Leicester, LE1 7RH, U.K.
    3School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, U.K.
    4School of Mathematics and Actuarial Science, University of Leicester, Leicester, LE1 7RH, U.K.
    5Key Laboratory of Behavior Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
    6Department of Psychology, University of the Chinese Academy of Sciences, Beijing 100101, China
    7Department of Infection Diseases, The Fourth People's Hospital of Huai'an, Huai'an 223002, China
    8Department of Computer Science, Georgia State University, Atlanta 30302-5060, U.S.A.
    9Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
  • Received:2020-06-03 Revised:2021-01-18 Accepted:2021-03-30 Online:2022-03-31 Published:2022-03-31
  • Contact: Yu-Dong Zhang E-mail:yudongzhang@ieee.org
  • About author:Yu-Dong Zhang received his Ph.D. degree from Southeast University, Nanjing, in 2010. He worked as a postdoc from 2010 to 2012 with Columbia University, New York, and as an assistant research scientist from 2012 to 2013 with Research Foundation of Mental Hygiene (RFMH), New York. Now he serves as a professor with University of Leicester, Leicester, UK. Prof. Zhang was the 2019 & 2021 recipient of "Web of Science Highly Cited Researcher".
  • Supported by:
    This paper was supported by the Royal Society International Exchanges Cost Share Award of UK under Grant No. RP202G0230, the Medical Research Council Confidence in Concept Award of UK under Grant No. MC_PC_17171, the Hope Foundation for Cancer Research of UK under Grant No. RM60G0680, the British Heart Foundation Accelerator Award of UK under Grant No. AA/18/3/34220, Sino-UK Industrial Fund under Grant No. RP202G0289, the Global Challenges Research Fund (GCRF) of UK under Grant No. P202PF11, the Fundamental Research Funds for the Central Universities of China under Grant No. CDLS-2020-03, the Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education of China, Henan Key Research and Development Project of China, under Grant No. 182102310629, and the National Natural Science Foundation of China under Grant Nos. U19B2032 and 61772511.

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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.


Key words: pneumonia; COVID-19; convolutional neural network; AlexNet; deep learning ;

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