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Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

“Electronic nose” technology, including technical and software tools to analyze gas mixtures, is promising regarding the diagnosis of malignant neoplasms. This paper presents the research results of breath samples analysis from 59 people, including patients with a confirmed diagnosis of respiratory...

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Autores principales: Chernov, Vladimir I., Choynzonov, Evgeniy L., Kulbakin, Denis E., Obkhodskaya, Elena V., Obkhodskiy, Artem V., Popov, Aleksandr S., Sachkov, Victor I., Sachkova, Anna S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7555125/
https://www.ncbi.nlm.nih.gov/pubmed/32899544
http://dx.doi.org/10.3390/diagnostics10090677
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author Chernov, Vladimir I.
Choynzonov, Evgeniy L.
Kulbakin, Denis E.
Obkhodskaya, Elena V.
Obkhodskiy, Artem V.
Popov, Aleksandr S.
Sachkov, Victor I.
Sachkova, Anna S.
author_facet Chernov, Vladimir I.
Choynzonov, Evgeniy L.
Kulbakin, Denis E.
Obkhodskaya, Elena V.
Obkhodskiy, Artem V.
Popov, Aleksandr S.
Sachkov, Victor I.
Sachkova, Anna S.
author_sort Chernov, Vladimir I.
collection PubMed
description “Electronic nose” technology, including technical and software tools to analyze gas mixtures, is promising regarding the diagnosis of malignant neoplasms. This paper presents the research results of breath samples analysis from 59 people, including patients with a confirmed diagnosis of respiratory tract cancer. The research was carried out using a gas analytical system including a sampling device with 14 metal oxide sensors and a computer for data analysis. After digitization and preprocessing, the data were analyzed by a neural network with perceptron architecture. As a result, the accuracy of determining oncological disease was 81.85%, the sensitivity was 90.73%, and the specificity was 61.39%.
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spelling pubmed-75551252020-10-14 Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors Chernov, Vladimir I. Choynzonov, Evgeniy L. Kulbakin, Denis E. Obkhodskaya, Elena V. Obkhodskiy, Artem V. Popov, Aleksandr S. Sachkov, Victor I. Sachkova, Anna S. Diagnostics (Basel) Article “Electronic nose” technology, including technical and software tools to analyze gas mixtures, is promising regarding the diagnosis of malignant neoplasms. This paper presents the research results of breath samples analysis from 59 people, including patients with a confirmed diagnosis of respiratory tract cancer. The research was carried out using a gas analytical system including a sampling device with 14 metal oxide sensors and a computer for data analysis. After digitization and preprocessing, the data were analyzed by a neural network with perceptron architecture. As a result, the accuracy of determining oncological disease was 81.85%, the sensitivity was 90.73%, and the specificity was 61.39%. MDPI 2020-09-05 /pmc/articles/PMC7555125/ /pubmed/32899544 http://dx.doi.org/10.3390/diagnostics10090677 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chernov, Vladimir I.
Choynzonov, Evgeniy L.
Kulbakin, Denis E.
Obkhodskaya, Elena V.
Obkhodskiy, Artem V.
Popov, Aleksandr S.
Sachkov, Victor I.
Sachkova, Anna S.
Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors
title Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors
title_full Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors
title_fullStr Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors
title_full_unstemmed Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors
title_short Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors
title_sort cancer diagnosis by neural network analysis of data from semiconductor sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7555125/
https://www.ncbi.nlm.nih.gov/pubmed/32899544
http://dx.doi.org/10.3390/diagnostics10090677
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