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Journal of Education, Health and Sport

Neural networks for the Recognition of X-ray Images of Ailments for Covid-19
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Neural networks for the Recognition of X-ray Images of Ailments for Covid-19

Authors

  • Liliia Diachenko Yuriy Fedkovych Chernivtsi National University, Kotsyubynsky 2, Chernivtsi, 58012
  • Oleksandr Lazoriak Yuriy Fedkovych Chernivtsi National University, Kotsyubynsky 2, Chernivtsi, 58012
  • Yurii Dobrovolsky aYuriy Fedkovych Chernivtsi National University, Kotsyubynsky 2, Chernivtsi, 58012
  • Georgii Prokhorov Yuriy Fedkovych Chernivtsi National University, Kotsyubynsky 2, Chernivtsi, 58012
  • Liliia Shumyliak Yuriy Fedkovych Chernivtsi National University, Kotsyubynsky 2, Chernivtsi, 58012

DOI:

https://doi.org/10.12775/JEHS.2022.12.06.002

Keywords

Convolutional neural network, computer vision, optimizers, X-rays, model, Covid-19, machine learning

Abstract

This investigation analyzes the current state of neural networks, considers the available types, optimizers used for training, describes their benefits and disadvantages. The task of computer vision is defined and the answer to the question why the use of neural networks is an important task today is given. The powerful neural network from Google was proposed as an example and its algorithm is described in detail. Studies have shown how to configure models to get high performance.

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Published

2022-04-26

How to Cite

1.
DIACHENKO, Liliia, LAZORIAK, Oleksandr, DOBROVOLSKY, Yurii, PROKHOROV, Georgii & SHUMYLIAK, Liliia. Neural networks for the Recognition of X-ray Images of Ailments for Covid-19. Journal of Education, Health and Sport [online]. 26 April 2022, T. 12, nr 6, s. 26–38. [accessed 30.3.2023]. DOI 10.12775/JEHS.2022.12.06.002.
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Issue

Vol. 12 No. 6 (2022)

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Research Articles

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Copyright (c) 2022 Liliia Diachenko, Oleksandr Lazoriak, Yurii Dobrovolsky, Georgii Prokhorov, Liliia Shumyliak

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The periodical offers access to content in the Open Access system under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0

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