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.

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 ;

[1] Wang C, Horby P W, Hayden F G, Gao G F. A novel coronavirus outbreak of global health concern. The Lancet, 2020, 395(10223): 470-473. DOI: 10.1016/S0140-6736(20)30185-9.
[2] Wang D, Hu B, Hu C et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA, 2020, 323(11): 1061-1069. DOI: 10.1001/jama.2020.1585.
[3] Lu Z, Lu S Y, Liu G et al. A pathological brain detection system based on radial basis function neural network. Journal of Medical Imaging and Health Informatics, 2016, 6(5): 1218-1222. DOI: 10.1166/jmihi.2016.1901.
[4] Yang J, Qiu X, Shi J P et al. A pathological brain detection system based on kernel based ELM. Multimedia Tools and Applications, 2018, 77(3): 3715-3728. DOI: 10.1007/s11042-016-3559-z.
[5] Lu S, Qiu X, Shi J P et al. A pathological brain detection system based on extreme learning machine optimized by bat algorithm. CNS & Neurological Disorders-Drug Targets, 2017, 16(1): 23-29. DOI: 10.2174/1871527315666161019153259.
[6] Wang S H, Li P, Chen P et al. Pathological brain detection via wavelet packet Tsallis entropy and real-coded biogeography-based optimization. Fundamenta Informaticae, 2017, 151(1/2/3/4): 275-291. DOI: 10.3233/FI-2017-1492.
[7] Jiang X, Zhang Y. Chinese sign language fingerspelling recognition via six-layer convolutional neural network with leaky rectified linear units for therapy and rehabilitation. Journal of Medical Imaging and Health Informatics, 2019, 9(9): 2031-2038. DOI: 10.1166/jmihi.2019.2804.
[8] Szegedy C, Liu W, Jia Y et al. Going deeper with convolutions. In Proc. the 2015 IEEE Conference on Computer Vision and Pattern Recognition, June 2015, pp.1-9. DOI: 10.1109/CVPR.2015.7298594.
[9] Yu X, Wang S H. Abnormality diagnosis in mammograms by transfer learning based on ResNet18. Fundamenta Informaticae, 2019, 168(2/3/4): 219-230. DOI: 10.3233/FI-2019-1829.
[10] Zhang Y D, Satapathy S C, Zhu L Y et al. A seven-layer convolutional neural network for chest CT based COVID-19 diagnosis using stochastic pooling. IEEE Sensors Journal. DOI: 10.1109/JSEN.2020.3025855.
[11] Wu S, Wu X, Zhang Yet al. Diagnosis of COVID-19 by wavelet renyi entropy and three-segment biogeography-based optimization. International Journal of Computational Intelligence Systems, 2020, 13(1): 1332-1344. DOI: 10.2991/ijcis.d.200828.001.
[10] Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238. DOI: 10.1109/TPAMI.2005.159.
[11] Chung M, Bernheim A, Mei X et al. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology, 2020, 295(1): 202-207. DOI: 10.1148/radiol.2020200230.
[12] Maghdid H S, Ghafoor K Z, Sadiq A S et al. A novel AI-enabled framework to diagnose coronavirus COVID 19 using smartphone embedded sensors: Design study. arXiv:2003.07434, 2020. https://arxiv.org/abs/2003.07434, Dec. 2020.
[13] Wang L, Wong A. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. arXiv:2003.09871, 2020. https://arxiv.org/abs/2003.09871, Dec. 2020.
[14] Al-Karawi D, Al-Zaidi S, Polus N, Jassim S. Machine learning analysis of chest CT scan images as a complementary digital test of coronavirus (COVID-19) patients. medRxiv. DOI: 10.1101/2020.04.13.20063479.
[15] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In Proc. the 25th International Conference on Neural Information Processing Systems, December 2012, pp.1097-1105. DOI: 10.1145/3065386.
[18] Shakarami A, Tarrah H, Mahdavi-Hormat A. A CAD system for diagnosing Alzheimer's disease using 2D slices and an improved AlexNet-SVM method. Optik, 2020, 212: Article No. 164237. DOI: 10.1016/j.ijleo.2020.164237.
[19] Wang R, Xu J, Han T X. Object instance detection with pruned Alexnet and extended training data. Signal Processing: Image Communication, 2019, 70: 145-156. DOI: 10.1016/j.image.2018.09.013.
[16] Szymak P, Gasiorowski M. Using pretrained AlexNet deep learning neural network for recognition of underwater objects. Na\v{s}e More, 2020, 67(1): 9-13. DOI: 10.17818/NM/2020/1.2.
[17] Guo C J, Xu Y L, Tian Z. Inversion of PM2.5 atmospheric refractivity profile based on AlexNet model from the perspective of electromagnetic wave propagation. Environmental Science and Pollution Research, 2020, 27(30): 37333-37346. DOI: 10.1007/s11356-020-07703-w.
[18] Zhao X Y, Dong C Y, Zhou P, Zhu M J, Ren J W, Chen X Y. Detecting surface defects of wind tubine blades using an Alexnet deep learning algorithm. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2019, E102A(12): 1817-1824. DOI: 10.1587/transfun.E102.A.1817.
[19] Xiao L, Yan Q, Deng S. Scene classification with improved AlexNet model. In Proc. the 12th International Conference on Intelligent Systems and Knowledge Engineering, Nov. 2017. DOI: 10.1109/ISKE.2017.8258820.
[20] Rakitianskaia A, Engelbrecht A. Measuring saturation in neural networks. In Proc. the 2015 IEEE Symposium Series on Computational Intelligence, Dec. 2015, pp.1423-1430. DOI: 10.1109/SSCI.2015.202.
[21] Gertych A, Swiderska-Chadaj Z, Ma Z et al. Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci. Rep., 2019, 9(1): Article No. 1483. DOI: 10.1038/s41598-018-37638-9.
[22] Fukae J, Isobe M, Hattori T et al. Convolutional neural network for classification of two-dimensional array images generated from clinical information may support diagnosis of rheumatoid arthritis. Sci. Rep., 2020, 10(1): Article No. 5648. DOI: 10.1038/s41598-020-62634-3.
[23] Nguyen H D, Lloyd-Jones L R, McLachlan G J. A universal approximation theorem for mixture-of-experts models. Neural Computation, 2016, 28(12): 2585-2593. DOI: 10.1162/NECO_a_00892.
[24] Huang Y, Yang D, Wang K, Wang L, Fan J. A quality diagnosis method of GMAW based on improved empirical mode decomposition and extreme learning machine. Journal of Manufacturing Processes, 2020, 54: 120-128. DOI: 10.1016/j.jmapro.2020.03.006.
[25] Schmidt W F, Kraaijveld M A, Duin R P W. Feedforward neural networks with random weights. In Proc. the 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Aug. 30-Sept. 3, 1992. DOI: 10.1109/ICPR.1992.201708.
[26] Pao Y H, Park G H, Sobajic D J. Learning and generalization characteristics of the random vector functional-link net. Neurocomputing, 1994, 6(2): 163-180. DOI: 10.1016/0925-2312(94)90053-1.
[27] Kushwah G S, Ranga V. Voting extreme learning machine based distributed denial of service attack detection in cloud computing. Journal of Information Security and Applications, 2020, 53: Article No. 102532. DOI: 10.1016/j.jisa.2020.102532.
[28] Yager R R, Kreinovich V. Universal approximation theorem for uninorm-based fuzzy systems modeling. Fuzzy Sets and Systems, 2003, 140(2): 331-339. DOI: 10.1016/S0165-0114(02)00521-3.
[29] Scardapane S, Fierimonte R, Wang D H, Panella M, Uncini A. Distributed music classification using random vector functional-link nets. In Proc. the 2015 International Joint Conference on Neural Networks, July 2015. DOI: 10.1109/IJCNN.2015.7280333.
[30] Chaudhuri A. The minimization of empirical risk through stochastic gradient descent with momentum algorithms. In Proc. the 8th Computer Science On-line Conference on Artificial Intelligence Methods in Intelligent Algorithms, April 2019, pp.168-181. DOI: 10.1007/978-3-030-19810-7_17.
[31] Dean J, Corrado G, Monga R et al. Large scale distributed deep networks. In Proc. the 25th International Conference on Neural Information Processing Systems, December 2012, pp.1223-1231.
[32] Rajaraman S, Candemir S, Kim I, Thoma G, Antani S. Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Applied Sciences, 2018, 8(10): Article No. 1715. DOI: 10.3390/app8101715.
[33] Ardila D, Kiraly A P, Bharadwaj S et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 2019, 25(6): 954-961. DOI: 10.1038/s41591-019-0447-x.
[34] Chae K J, Jin G Y, Ko S B, Wang Y, Zhang H, Choi E J, Choi H. Deep learning for the classification of small (≤2 cm) pulmonary nodules on CT imaging: A preliminary study. Acad. Radiol., 2020, 27(4): e55-e63. DOI: 10.1016/j.acra.2019.05.018.
[35] Koo H J, Lim S, Choe J, Choi S H, Sung H, Do K H. Radiographic and CT features of viral pneumonia. RadioGraphics, 2018, 38(3): 719-739. DOI: 10.1148/rg.2018170048.
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