verifiedOpen Access Peer-Reviewed Scientific Journal (CC BY 4.0)
Submit Manuscript|OJS Login|Register
Peer-Reviewed Academic JournalInternational Journal of Applied Methods in Electronics and Computers
ISSN: 3023-4409DOI Prefix: 10.58190/ijamec
search
person
Research ArticlesverifiedPeer-ReviewedOpen Access
Pages: 97-101

Detection and differentiation of COVID-19 using deep learning approach fed by x-rays

Çağatay Berke ERDAŞ fingerprint•
Didem ÖLÇER fingerprint
Publication DateOctober 1, 2020
Volume / IssueVol. 8, No. 3 (pp. 97-101)
subject

Abstract

The coronavirus, which appeared in China in late 2019, spread over the world and became an epidemic. Although the mortality rate is not very high, it has hampered the lives of people around the world due to the high rate of spread. Moreover, compared to other individuals in the society, the mortality rate in elderly individuals and people with chronic disease is high. The early detection of infected individuals is one of the most effective ways to both fight disease and slow the outbreak. In this study, a deep learning approach, which is alternative and supportive of traditional diagnostic tools and fed with chest x-rays, has been developed. The purpose of this deep learning approach, which has the convolutional neural networks (CNNs) architecture, is (1) to diagnose pneumonia caused by a coronavirus, (2) to find out if a patient with symptoms of pneumonia on chest X-ray is caused by bacteria or coronavirus. For this purpose, a new database has been brought together from various publicly available sources. This dataset includes 50 chest X-rays from people diagnosed with pneumonia caused by a coronavirus, 50 chest X-rays from healthy individuals belonging to the control group, and 50 chest X-rays from people diagnosed with bacterium from pneumonia. Our approach succeeded in terms of accuracy of 92% for corona virus-based pneumonia diagnosis tasks (1) and 81% for the task of finding the origin of pneumonia (2). Besides, achievements for Area Under the ROC Curve (ROC_AUC), Precision, Recall, F1-score, Specificity, and Negative Predictive Value (NPV) metrics are specified in this paper.
Keywords:AlexNetCNNCOVID-19PneumoniaX-ray

Author Affiliations

format_list_numbered

References (24)

Cited Literature
  1. 1

    R. F. Sear, et al., “Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning,” in IEEE Access, vol. 8, pp. 91886-91893, 2020, DOI: 10.1109/ACCESS.2020.2993967.

    open_in_newCrossref / DOI: 10.1109/ACCESS.2020.2993967
  2. 2

    M. Abdel-Basset, R. Mohamed, M. Elhoseny, R. K. Chakrabortty, and M. Ryan, “A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy,” in IEEE Access, vol. 8, pp. 79521-79540, 2020, doi: 10.1109/ACCESS.2020.2990893.

    open_in_newCrossref / DOI: 10.1109/ACCESS.2020.2990893
  3. 3

    T.P. Velavan, C. G. Meyer, “The COVID-19 epidemic,” Trop Med Int Health, vol. 25, pp. 278-280, 2020, doi:10.1111/tmi.13383

    open_in_newCrossref / DOI: 10.1111/tmi.13383
  4. 4

    C. Sohrabi, et al., “World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19),” Int J Surg, vol. 76, pp. 71-76, 2020, doi:10.1016/j.ijsu.2020.02.034.

    open_in_newCrossref / DOI: 10.1016/j.ijsu.2020.02.034
  5. 5

    S. Kannan, A. P. Shaik Syed, A. Sheeza, K. Hemalatha, “COVID-19 (Novel Coronavirus 2019) - recent trends,” Eur Rev Med Pharmacol Sci, vol. 24, pp. 2006-2011, 2020, doi:10.26355/eurrev_202002_20378.

    open_in_newCrossref / DOI: 10.26355/eurrev_202002_20378
  6. 6

    D. G. Ahn, H. J. Shin, M. H. Kim, et al., “Current Status of Epidemiology, Diagnosis, Therapeutics, and Vaccines for Novel Coronavirus Disease 2019 (COVID-19),” J Microbiol Biotechnol, vol. 30, pp. 313-324, 2020, doi:10.4014/jmb.2003.03011.

    open_in_newCrossref / DOI: 10.4014/jmb.2003.03011
  7. 7

    H.A Rothan, S. N. Byrareddy, “The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak,” J Autoimmun, pp.102433, 2020, doi:10.1016/j.jaut.2020.102433.

    open_in_newCrossref / DOI: 10.1016/j.jaut.2020.102433
  8. 8

    P. Sun, X. Lu, C. Xu, W. Sun, B. Pan, “Understanding of COVID-19 based on current evidence,” J Med Virol, vol. 92, pp. 548-551, 2020, doi:10.1002/jmv.25722.

    open_in_newCrossref / DOI: 10.1002/jmv.25722
  9. 9

    C. Zhan, C. K. Tse, Z. Lai, T. Haa, J. Su, “Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding,” PLOS ONE, vol.15, pp. e0234763, 2020, https://doi.org/10.1371/journal.pone.0234763.

    open_in_newCrossref / DOI: 10.1371/journal.pone.0234763
  10. 10

    V. Marmarelis, “Predictive modeling of COVID-19 data in the US: Adaptive phase-space approach,” in IEEE Open Journal of Engineering in Medicine and Biology, doi: 10.1109/OJEMB.2020.3008313.

    open_in_newCrossref / DOI: 10.1109/OJEMB.2020.3008313
  11. 11

    H. Kang et al., “Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning,” in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2020.2992546.

    open_in_newCrossref / DOI: 10.1109/TMI.2020.2992546
  12. 12

    M. B. Jamshidi et al., “Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment,” in IEEE Access, vol. 8, pp. 109581-109595, 2020, doi: 10.1109/ACCESS.2020.3001973.

    open_in_newCrossref / DOI: 10.1109/ACCESS.2020.3001973
  13. 13

    M. A. Elaziz et al., “An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-level Thresholding: Real World Example of COVID-19 CT Image Segmentation,” in IEEE Access, doi: 10.1109/ACCESS.2020.3007928.

    open_in_newCrossref / DOI: 10.1109/ACCESS.2020.3007928
  14. 14

    D. Dong et al., "The role of imaging in the detection and management of COVID-19: a review," in IEEE Reviews in Biomedical Engineering, doi: 10.1109/RBME.2020.2990959.

    open_in_newCrossref / DOI: 10.1109/RBME.2020.2990959
  15. 15

    P.K. Sethy, S.K. Behera, “Detection of Coronavirus Disease (COVID-19) Based on Deep Features and Support Vector Machine,” Preprints 2020, 2020030300, 2020.

  16. 16

    Xu, Xiaowei, et al. “Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia,” arXiv preprint, 2020, arXiv:2002.09334.

  17. 17

    X. Wang et al., "A Weakly-supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2020.2995965.

    open_in_newCrossref / DOI: 10.1109/TMI.2020.2995965
  18. 18

    E.E.D. Hemdan, M.A. Shouman, M.E. Karar, “COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images,” Image and Video Processing, 2020.

  19. 19

    D. Ioannis, T. Bessiana, “COVID-19: Automatic Detection from X-Ray Images Utilizing Transfer Learning with Convolutional Neural Networks,” Physical and Engineering Sciences in Medicine, vol.43, pp. 635–640, 2020, doi: https://doi.org/10.1007/s13246-020-00865-4.

    open_in_newCrossref / DOI: 10.1007/s13246-020-00865-4
  20. 20

    A. Narin, C. Kaya, Z. Pamuk, “Automatic Detection of Coronavirus Disease (COVID-19) Using X-Ray Images and Deep Convolutional Neural Networks,” arXiv preprint, arXiv:2003.10849, 2020

  21. 21

    S. Ren, K. He, R. Girshick and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017, doi: 10.1109/TPAMI.2016.2577031.

    open_in_newCrossref / DOI: 10.1109/TPAMI.2016.2577031
  22. 22

    H. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285-1298, May 2016, doi: 10.1109/TMI.2016.2528162.

    open_in_newCrossref / DOI: 10.1109/TMI.2016.2528162
  23. 23

    A. Krizhevsky, I. Sutskever, G.E. Hinton, “Image Net Classification with Deep Convolutional Neural Network,” NIPS, 2012.

  24. 24

    Z. Alom, et. al., “The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches,” Computer Vision and Pattern Recognition, 2018.

format_quote

How to Cite This Article

Ç. B. E., D. Ö. (2020). Detection and differentiation of COVID-19 using deep learning approach fed by x-rays. International Journal of Applied Methods in Electronics and Computers, 97-101. https://doi.org/10.18100/ijamec.799651