Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

Vladimir I. Chernov, Evgeniy L. Choynzonov, Denis E. Kulbakin, Elena V. Obkhodskaya, Artem V. Obkhodskiy, Aleksandr S. Popov, Victor I. Sachkov, Anna S. Sachkova

Отделение ядерно-топливного цикла

Результат исследований: Материалы для журнала › Статья › рецензирование

Аннотация

“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%.

Язык оригинала	Английский
Номер статьи	677
Журнал	Diagnostics
Том	10
Номер выпуска	9
DOI	https://doi.org/10.3390/diagnostics10090677
Состояние	Опубликовано - сен 2020

ASJC Scopus subject areas

Clinical Biochemistry

Доступ к документу

10.3390/diagnostics10090677

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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.

В: Diagnostics, Том 10, № 9, 677, 09.2020.

Результат исследований: Материалы для журнала › Статья › рецензирование

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abstract = "“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%.",

keywords = "Classification, Electronic nose, Gas analyzer, Malignant neoplasm, Metal oxide semiconductor sensor, Neural network",

author = "Chernov, {Vladimir I.} and Choynzonov, {Evgeniy L.} and Kulbakin, {Denis E.} and Obkhodskaya, {Elena V.} and Obkhodskiy, {Artem V.} and Popov, {Aleksandr S.} and Sachkov, {Victor I.} and Sachkova, {Anna S.}",

year = "2020",

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AU - Chernov, Vladimir I.

AU - Choynzonov, Evgeniy L.

AU - Kulbakin, Denis E.

AU - Obkhodskaya, Elena V.

AU - Obkhodskiy, Artem V.

AU - Popov, Aleksandr S.

AU - Sachkov, Victor I.

AU - Sachkova, Anna S.

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AB - “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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KW - Gas analyzer

KW - Malignant neoplasm

KW - Metal oxide semiconductor sensor

KW - Neural network

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