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Chest X-ray image classification for viral pneumonia and Сovid-19 using neural networks
V.G. Efremtsev 1, N.G. Efremtsev 1, E.P. Teterin 2, P.E. Teterin 3, E.S. Bazavluk 1

Independent researcher,
Kovrov State Technological Academy named after V.A.Degtyarev, Kovrov, Vladimir region, Russia,
National Research Nuclear University "MEPhI", Moscow, Russia

 PDF, 981 kB

DOI: 10.18287/2412-6179-CO-765

Pages: 149-153.

Full text of article: Russian language.

The use of neural networks to detect differences in radiographic images of patients with pneu-monia and COVID-19 is demonstrated. For the optimal selection of resize and neural network ar-chitecture parameters, hyperparameters, and adaptive image brightness adjustment, precision, recall, and f1-score metrics are used. The high values of these metrics of classification quality (> 0.91) strongly indicate a reliable difference between radiographic images of patients with pneumonia and patients with COVID-19, which opens up the possibility of creating a model with good predictive ability without involving ready-to-use complex models and without pre-training on third-party data, which is promising for the development of sensitive and reliable COVID-19 express-diagnostic methods.

X-ray image processing, convolutional neural network, classification, COVID-19.

Efremtsev VG, Efremtsev NG, Teterin EP, Teterin PE, Bazavluk ES. Chest x-ray image classification for viral pneumonia and Covid-19 using neural networks. Computer Optics 2021; 45(1): 149-153. DOI:10.18287/2412-6179-CO-765.

The authors thank for the support from the National Research Nuclear University MEPhI in the framework of the Russian Academic Excellence Project (contract No. 02.a03.21.0005, 27.08.2013).


  1. Wu F, Zhao S, Yu B, et al. A new coronavirus associated with human respiratory disease in China. Nature 2020; 579(7798): 265.
  2. World Health Organization. Pneumonia of unknown cause – China. Source: <https://www.who.int/csr/don/05-january-2020-pneumonia-of-unkown-cause-china/en/>.
  3. Veselova EI, Russkikh AE, Kaminskiy GD, Lovacheva OV, Samoylova AG, Vasilyeva IA. Novel coronavirus infection [In Russian]. Tuberculosis and Lung Diseases 2020; 98(4): 6-14. DOI: 10.21292/2075-1230-2020-98-4-6-14.
  4. Pashina TA, Gaidel AV, Zelter PM, Kapishnikov AV, Nikonorov AV. Automatic highlighting of the region of interest in computed tomography images of the lungs. Computer Optics 2020; 44(1): 74-81. DOI: 10.18287/2412-6179-CO-659.
  5. Li L, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy. Radiology 2020; 296(2): E65-E71. DOI: 10.1148/radiol.2020200905.
  6. Nath M, Choudhury C. Automatic detection of pneumonia from chest X-Rays using deep learning. In Book: Bhattacharjee A, Borgohain S, Soni B, Verma G, Gao X-Z, eds. Machine learning, image processing, network security and data sciences. Singapore: Springer; 2020: 175-182. DOI: 10.1007/978-981-15-6315-7_14.
  7. Okeke S, et al. An efficient deep learning approach to pneumonia classification in healthcare. J Healthc Eng 2019; 2019: 4180949. DOI: 10.1155/2019/4180949.
  8. Swapnarekha H, et al. Role of intelligent computing in COVID-19 prognosis: A state-of-the-art revie. Chaos Solitons Fractals 2020; 138: 109947. DOI: 10.1016/j.chaos.2020.109947.
  9. Wang L, Wong A. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-Ray images. 2020. Source: <https://arxiv.org/abs/2003.09871>.
  10. Ozturk T, et al. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 2020; 121: 103792. DOI: 10.1016/j.compbiomed.2020.103792.
  11. Loey M, Smarandache F, Khalifa NEM. Within the lack of chest COVID-19 X-Ray dataset: A novel detection model based on GAN and deep transfer learning. Symmetry 2020; 12: 651. DOI: 10.3390/sym12040651.
  12. Das D, Santosh KC, Pal U. Truncated inception net: COVID-19 outbreak screening using chest X-Rays. Phys Eng Sci Med 2020; 43(3): 915-925. DOI: 10.1007/s13246-020-00888-x.
  13. Apostolopoulos ID, Mpesiana TA. COVID-19: automatic detection from X-Ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 2020;43(2): 635-640. DOI: 10.1007/s13246-020-00865-4.
  14. Tuncer T, Dogan S, Ozyurt F. An automated residual exemplar local binary pattern and iterative relieff based COVID-19 detection method using chest X-Ray image. Chemom Intell Lab Syst 2020; 203: 104054. DOI: 10.1016/j.chemolab.2020.104054.
  15. CoronaHack -Chest X-Ray-Dataset. Classify the X-Ray image which is having Corona. Source: <https://www.kaggle.com/praveengovi/coronahack-chest-xraydataset>
  16. Gonzalez RC, Woods RE. Digital image processing. 3rd ed. Pearson Education Inc; 2008.
  17. Chollet F. Deep learning with Python. New York: Manning Publications; 2017.
  18. Müller AC, Guido S. Introduction to machine learning with Python: A guide for data scientists. O'Reilly Media; 2016.
  19. Géron A. Hands-on machine learning with Scikit-Learn and TensorFlow: Сoncepts, tools, and techniques to build intelligent systems. Sebastopol, CA: O'Reilly Media; 2017.

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