Automatic detection lung infected COVID-19 disease using deep learning (Convolutional Neural Network)

Document Type : Research Paper

Authors

1 Department of Computer Science, Faculty of Computer Science and Maths, University of Kufa, Najaf, Iraq

2 University of Thi-Qar, 64001 Al-Nassiriya, Iraq

3 Education Directorate of Thi-Qar, Ministry of Education, Iraq

Abstract

In late 2019,  a virus appeared suddenly he claims Covid-19, which started in China and began to spread very widely around the world. And because of its effects, which are not limited to human life only, but rather in economic and social aspects, and because of the increase in daily injuries and significantly with the limited hospitals that cannot accommodate these large numbers, it is necessary to find an automatic and rapid detection method that limits the spread of the disease and its detection at an early stage in order to be treated more quickly. In this paper, deep learning was relied upon to create a CNN model to detect COVID-19 infected lungs using chest X-ray images. The base consists of a set of images taken of lungs infected with Covid-19 disease and normal lungs, as the CNN structure gave accuracy, Precision, Recall and F-Measure 100%.

Keywords

[1] M. Akagi, Y. Nakamura, T. Higaki and et al., Correction to: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT[J], European Radiology, 29 (2019).
[2] J. Chan, S. Yuan, K.H. Kok and et al., A familial cluster of COVID-19 associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster[J], Lancet. 2020 Jan 24. pii: S0140- 6736(20)30154-9. doi: 10.1016/S0140-6736(20)30154-9.
[3] A. Gad, Practical Computer Vision Applications Using Deep Learning with CNNs, (2018).
[4] B. Gharbi, S. Micha¨el, J. Chen, J. Barron and et al., Deep Bilateral Learning for Real-Time Image Enhancement[J], Acm Transactions on Graphics, 36 (2017) 118.
[5] S. Hassantabar, M. Ahmadi and A. Sharifi, Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches, Chaos, Solitons and Fractals, 140 (2020) 110170.
[6] M. Hesamian, W. Jia, X. He and et al., Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges[J], Journal of Digital Imaging, 32 (2019).
[7] C. Huang, Y. Wang, X. Li and et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China[J], Lancet. 2020 Jan 24. pii: S0140-6736(20)30183-5. doi:10.1016/S0140-6736(20)30183-5.
[8] Q. Li, X. Guan, P. Wu and et al., Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected COVID-19[J], N Engl J Med. 2020 Jan 29. doi: 10.1056/NEJMoa2001316.
[9] P. Liu, L. Shi, W. Zhang, J. He, C. Liu, C. Zhao and L. Hu, Prevalence and genetic diversity analysis of human coronaviruses among cross-border children. Virology journal, 14 (2017) 1-8.
[10] B. Moons, D. Bankman and M. Verhelst, Embedded Deep Learning, (2018).
[11] J. Nagi, F. Ducatelle, G. Di Caro, D. Cire¸san, U. Meier, A. Giusti and L. Gambardella, Max-pooling convolutional neural networks for vision-based hand gesture recognition, IEEE International Conference on Signal and Image Processing Applications (ICSIPA), (2011) 342-347.
[12] A. Narin, C. Kaya and Z. Pamuk, Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks, arXiv preprint arXiv:2003.10849, (2020).
[13] A. Narin, C. Kaya and Z. Pamuk, Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks, arXiv preprint arXiv:2003.10849.A. F. Gad, Practical Computer Vision Applications Using Deep Learning with CNNs, 2018.
[14] M. Rahimzadeh and A. Attar, A new modified deep convolutional neural network for detecting COVID-19 from X-ray images, arXiv preprint arXiv:2004.08052, (2020).
[15] L. Wang, Z. Lin and A. Wong, Covid-net: A tailored deep convolutional neural network design for detection of covid 19 cases from chest x-ray images, Scientific Reports, 10 (2020) 1-12.
[16] A. Zhavoronkov, V. Aladinskiy, A. Zhebrak and et al., Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches, Insilico Medicine Hong Kong Ltd A 307 (2020): E1.
[17] N. Zhu, D. Zhang, W. Wang and et al., A Novel Coronavirus from Patients with COVID-19 in China, 2019[J], N Engl J Med. 2020 Jan 24. doi: 10.1056/NEJMoa2001017.
[18] https://www.pyimagesearch.com/2020/03/16/detecting-covid-19-in-x-ray-images learning/ fbclid=IwAR0UNoeVISUBISFDyJi5UX9KGFGn4dJAHHW2AvCz6ILXcv6dwLH8QBsG18].
[19] https://www.kaggle.com/paultimothymooney/chest-xray-COVID-19
[20] https://github.com/ieee8023/covid-chestxray-dataset.
Volume 12, Issue 2
November 2021
Pages 921-929
  • Receive Date: 10 April 2021
  • Revise Date: 13 May 2021
  • Accept Date: 26 May 2021