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

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

Fingerprint Подробные сведения о темах исследования «Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors». Вместе они формируют уникальный семантический отпечаток (fingerprint).

Цитировать

Chernov, V. I., Choynzonov, E. L., Kulbakin, D. E., Obkhodskaya, E. V., Obkhodskiy, A. V., Popov, A. S., Sachkov, V. I., & Sachkova, A. S. (2020). Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors. Diagnostics, 10(9), [677]. https://doi.org/10.3390/diagnostics10090677

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

@article{ef1fb1475c644eb699fdbb72013b8a9c,

title = "Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors",

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",

month = sep,

doi = "10.3390/diagnostics10090677",

language = "English",

volume = "10",

journal = "Diagnostics",

issn = "2075-4418",

publisher = "MDPI AG",

number = "9",

}

TY - JOUR

T1 - Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

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.

PY - 2020/9

Y1 - 2020/9

N2 - “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%.

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%.

KW - Classification

KW - Electronic nose

KW - Gas analyzer

KW - Malignant neoplasm

KW - Metal oxide semiconductor sensor

KW - Neural network

UR - http://www.scopus.com/inward/record.url?scp=85090724394&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85090724394&partnerID=8YFLogxK

U2 - 10.3390/diagnostics10090677

DO - 10.3390/diagnostics10090677

M3 - Article

AN - SCOPUS:85090724394

VL - 10

JO - Diagnostics

JF - Diagnostics

SN - 2075-4418

IS - 9

M1 - 677

ER -