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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 Сovid-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).


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