COVID-19 Screening Based on Application of Neural Network Classification of Exhale Spectra

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

This article describes the use of convolutional neural networks to screening first stage of the COVID on exhale spectra. A distinctive feature is the use of the glow-dicharge optical spectroscopy. The hypothesis put forward about the use of spectra images, and not the spectra themselves, for classification was confirmed. Accuracy was 87 %. However, accuracy is affected by obtaining stable exhale spectra. The impact on the spectrum of concomitant diseases, smoking, pregnancy is not fully understood. However, CNN can be used to diagnose COVID with an acceptable level of accuracy. The results described in the work are the initial stage of research.

Original languageEnglish
Title of host publicationProceedings of 2022 3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022
EditorsS. Shaposhnikov
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages24-27
Number of pages4
ISBN (Electronic)9781665467766
DOIs
Publication statusPublished - 2022
Event3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022 - St. Petersburg, Russian Federation
Duration: 16 Jun 2022 → …

Publication series

NameProceedings of 2022 3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022

Conference

Conference3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022
Country/TerritoryRussian Federation
CitySt. Petersburg
Period16.06.2022 → …

Keywords

  • CNN
  • COVID-19
  • disease classification
  • exhale spectra
  • glow-discharge
  • neural network

OECD FOS+WOS

  • 1.02 COMPUTER AND INFORMATION SCIENCES

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