Vol. 23 No. 4 (2020) Cover Image
Vol. 23 No. 4 (2020)

Published: December 31, 2020

Pages: 408-415

Articles

Coronavirus 2019 (COVID-19) Detection Based on Deep Learning

Toqa Abd Ul-Mohsen Sadoon 1 Mohammed Hussein Ali 2
1 Iraq
2 Electronic and Communication Engineering, Al-Nahrain University, Baghdad, Iraq.
History
  • Received: August 19, 2020
  • Revised: October 18, 2020
  • Accepted: November 21, 2020
  • Available Online: December 25, 2020
DOI

https://doi.org/10.29194/NJES.23040408

Abstract

Deep learning modeling could provide to detected Corona Virus 2019 (COVID-19) which is a critical task these days to make a treatment decision according to the diagnostic results. On the other hand, advances in the areas of artificial intelligence, machine learning, deep learning, and medical imaging techniques allow demonstrating impressive performance, especially in problems of detection, classification, and segmentation. These innovations enabled physicians to see the human body with high accuracy, which led to an increase in the accuracy of diagnosis and non-surgical examination of patients. There are many imaging models used to detect COVID-19, but we use computerized tomography (CT) because is commonly used. Moreover, we use for detection a deep learning model based on convolutional neural network (CNN) for COVID-19 detection. The dataset has been used is 544 slice of CT scan which is not sufficient for high accuracy, but we can say that it is acceptable because of the few datasets available in these days. The proposed model achieves validation and test accuracy 84.4% and 90.09%, respectively. The proposed model has been compared with other models to prove superiority of our model over the other models.

Keywords

References

  1. Wu F, Zhao S, Yu B, et al. “A new coronavirus associated with human respiratory disease in China”. Nature 2020; published online Feb 3. DOI:10.1038/s41586-020-2008-3.
  2. Huang C, Wang Y, Li X, et al. Clinical features patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395: 497–506 [doi.org/10.1016/S0140-6736(20)30183-5]
  3. Hui D S, Madani T A, Ntoumi F, et al. The continuing COVID-19 epidemic threat of novel coronaviruses to global health-The latest 2019 novel coronavirus outbreak in Wuhan, China. International journal of infectious diseases: IJID: official publication of the International Society for Infectious Diseases 2020;91:264.M. Wegmuller, J. P. von der Weid, P. Oberson, and N. Gisin, “High resolution fiber distributed measurements with coherent OFDR,” in Proc. ECOC’00, 2000, paper 11.3.4, p. 109.
  4. Holshue ML, DeBolt C, Lindquist S,et al. First Case of 2019 Novel Coronavirus in the United States. N Engl J Med 2020 Jan 31. doi:10.1056/NEJMoa2001191.
  5. Rothe C, Schunk M, Sothmann P, et al. Transmission of COVID-19 Infection from an 366 Asymptomatic Contact in Germany. N Engl J Med 2020 Jan 30. doi:10.1056/NEJMc2001468.
  6. Paules CI, Marston HD, Fauci AS. Coronavirus infections—more than just the common cold. JAMA. Published online January 23, 2020. doi:10.1001/jama.2020.07571.
  7. Callaway E. Time to use the p-word? Coronavirus enter dangerous new phase. Nature 2020; 579: 12.
  8. Chan JF, Yuan S, Kok KH, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-toperson transmission: a study of a family cluster. Lancet 2020; 395: 514–23.
  9. https://www.who.int/docs/defaultsource/coronaviruse/situation-reports/20200816-covid-19-sitrep-209.pdf?sfvrsn=5dde1ca2_2
  10. Guan H, Xiong Y, Shen N, et. Clinical and thin section CT features of patients with COVID-19 pneumonia in Wuhan. Radiologic Practice 2020.
  11. Gabor. T. Herman, “Fundamentals of Computerized Tomography: Image Reconstruction from Projections” second edition Springer 2009.
  12. Marwa. T. Al Hussani and Mohammed H. Ali Al Hayani ,”THE USE OF FILTERED BACKPROJECTION ALGORITHM FOR RECONSTRUCTION OF TOMOGRAPHIC IMAGE” Al-Nahrain University, Collegeof Engineering Journal (NUCEJ) Vol.17 No.2, 2014 pp.151-156.
  13. MI Razzak, S Naz, A Zaib “ Deep Learning for Medical Image Processing: Overview, Challenges and Future “ Classification in BioApps, 2018 – Springer.[ doi.org/10.1007/978-3-319-65981-7_12]
  14. Phil Kim, 'MatLab Deep Learning with Machine Learning, Neural Networks and Artificial Intelligence’, Seoul (2017).
  15. Yann LeCun, Yoshua Bengio, Geoffrey Hinton, 2015, 'Deep learning', Nuture, vol 5 2 1.
  16. Krizhevsky, Alex, “ImageNet Classification with Deep Convolutional Neural Networks,” 17 November 2013.
  17. Shuai Wang, et al. “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)’’DOI: 10.1101/2020.02.14.20023028.
  18. Xiaowei Xu1, et al, “ Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia”, 2020 - arxiv.org
  19. https://developer.nvidia.com/deep-learning-frameworks
  20. https://drive.google.com/file/d/1odxJF4kyHEtBqhkvz3iXpV3iQK34m6z0/view
  21. 21. R. Ramani,N.Suthanthira Vanitha,S. Valarmathy, 2013, 'The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images', International Journal of Image, Graphics and Signal Processing, vol. 5, no. 5, pp. 47-54.[ https://doi.org/10.5815/ijigsp.2013.05.06].
  22. Ian Goodfellow, Yoshua Bengio, Aaron Courville, 'Deep Learning', Google books, https://books.google.iq/books?id=Np9SDQAAQBAJ&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false
  23. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10, pp. 807–814. USA (2010).
  24. Ghosh, A., Sufian, A., Sultana, F., Chakrabarti, A., & De, D. (2020). Fundamental Concepts of Convolutional Neural Network. In Recent Trends and Advances in Artificial Intelligence and Internet of Things (pp. 519-567). Springer, Cham. [https://doi.org/10.1007/978-3-030-32644-9_36]
  25. S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” CoRR, vol. abs/1502.03167, 2015.
  26. Reza Bosagh Zadeh, Bharath Ramsundar, TensorFlow for Deep Learning, https://www.oreilly.com/library/view/tensorflow-for-deep/9781491980446/ch04.html.