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Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

1
Tomsk National Research Medical Center of the Russian Academy of Sciences, Cancer Research Institute, 5 Kooperativny Street, 634009 Tomsk, Russia
2
Laboratory of Chemical Technologies, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia
3
School of Nuclear Science & Engineering, National Research Tomsk Polytechnic University, 30 Lenin Avenue, 634050 Tomsk, Russia
*
Authors to whom correspondence should be addressed.
Diagnostics 2020, 10(9), 677; https://doi.org/10.3390/diagnostics10090677
Received: 7 August 2020 / Revised: 19 August 2020 / Accepted: 4 September 2020 / Published: 5 September 2020
(This article belongs to the Section Point-of-Care Diagnostics and Devices)
“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%. View Full-Text
Keywords: malignant neoplasm; classification; electronic nose; neural network; metal oxide semiconductor sensor; gas analyzer malignant neoplasm; classification; electronic nose; neural network; metal oxide semiconductor sensor; gas analyzer
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MDPI and ACS Style

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

AMA Style

Chernov VI, Choynzonov EL, Kulbakin DE, Obkhodskaya EV, Obkhodskiy AV, Popov AS, Sachkov VI, Sachkova AS. Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors. Diagnostics. 2020; 10(9):677. https://doi.org/10.3390/diagnostics10090677

Chicago/Turabian Style

Chernov, Vladimir I., Evgeniy L. Choynzonov, Denis E. Kulbakin, Elena V. Obkhodskaya, Artem V. Obkhodskiy, Aleksandr S. Popov, Victor I. Sachkov, and Anna S. Sachkova. 2020. "Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors" Diagnostics 10, no. 9: 677. https://doi.org/10.3390/diagnostics10090677

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