Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
COVID-19 Screening Based on Application of Neural Network Classification of Exhale Spectra. / Kugaevskikh, Alexander V.
Proceedings of 2022 3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022. ed. / S. Shaposhnikov. Institute of Electrical and Electronics Engineers Inc., 2022. p. 24-27 (Proceedings of 2022 3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - COVID-19 Screening Based on Application of Neural Network Classification of Exhale Spectra
AU - Kugaevskikh, Alexander V.
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - CNN
KW - COVID-19
KW - disease classification
KW - exhale spectra
KW - glow-discharge
KW - neural network
UR - http://www.scopus.com/inward/record.url?scp=85134290816&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/8166bf3b-cbdf-3158-81e1-b093e554002b/
U2 - 10.1109/NeuroNT55429.2022.9805563
DO - 10.1109/NeuroNT55429.2022.9805563
M3 - Conference contribution
AN - SCOPUS:85134290816
SN - 9781665467766
T3 - Proceedings of 2022 3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022
SP - 24
EP - 27
BT - Proceedings of 2022 3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022
A2 - Shaposhnikov, S.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022
Y2 - 16 June 2022
ER -
ID: 36717382