AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Journal of Computer Science and Technology Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Regular Paper

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

Department of Medical Imaging, The Fourth People's Hospital of Huai'an, Huai'an 223002, China
School of Informatics, University of Leicester, Leicester, LE1 7RH, U.K.
School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, U.K.
School of Mathematics and Actuarial Science, University of Leicester, Leicester, LE1 7RH, U.K.
Key Laboratory of Behavior Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
Department of Psychology, University of the Chinese Academy of Sciences, Beijing 100101, China
Department of Infection Diseases, The Fourth People's Hospital of Huai'an, Huai'an 223002, China
Department of Computer Science, Georgia State University, Atlanta 30302-5060, U.S.A.
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University Jeddah 21589, Saudi Arabia

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

Show Author Information

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.

Keywords

pneumoniaCOVID-19convolutional neural networkAlexNetdeep learning

Electronic Supplementary Material

Download File(s)
jcst-37-2-330-Highlights.pdf (192.6 KB)

References

[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref
[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.
Crossref
[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.
Crossref
[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.
Crossref
[16]

Szymak P, Gasiorowski M. Using pretrained AlexNet deep learning neural network for recognition of under-water objects. Naše More, 2020, 67(1): 9-13. DOI: 10.17818/NM/2020/1.2.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref
[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.
Crossref
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[25]
Schmidt W F, Kraaijveld M A, Duin R P W. Feed-forward 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.
Crossref
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref
[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.
Crossref
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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 (62 cm) pulmonary nodules on CT imaging: A preliminary study. Acad. Radiol., 2020, 27(4): e55-e63. DOI: 10.1016/j.acra.2019.05.018.
Crossref Google Scholar
[35]

Koo H J, Lim S, Choe J, Choi S H, Sung H, Do K H. Radio-graphic and CT features of viral pneumonia. RadioGraphics, 2018, 38(3): 719-739. DOI: 10.1148/rg.2018170048.
Crossref Google Scholar
Journal of Computer Science and Technology
Volume 37 Issue 2,
March 2022
Pages 330-343
DOI: 10.1007/s11390-020-0679-8
Cite this article:
Zhang X, Lu S, Wang S-H, et al. Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture. Journal of Computer Science and Technology, 2022, 37(2): 330-343. https://doi.org/10.1007/s11390-020-0679-8

392

Views

44

Crossref

39

Web of Science

37

Scopus

0

CSCD

Altmetrics
Received: 03 June 2020
Accepted: 30 March 2021
Published: 31 March 2022
©Institute of Computing Technology, Chinese Academy of Sciences 2022
Return