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
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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 -