Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors; Diagnostics; Vol. 10, iss. 9

Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors; Diagnostics; Vol. 10, iss. 9

Bibliographic Details
Parent link:	Diagnostics Vol. 10, iss. 9.— 2020.— [677, 11 p.]
Corporate Author:	Национальный исследовательский Томский политехнический университет Инженерная школа ядерных технологий Отделение ядерно-топливного цикла
Other Authors:	Chernov V. I. Vladimir Ivanovich, Choynzonov E. L. Evgeny Lkhamatsyrenovich, Kulbakin D. E. Denis Evgenjevich, Obkhodskaya E. V. Elena Vladimirovna, Obkhodskiy A. V. Artem Viktorovich, Popov A. S. Aleksandr Sergeevich, Sachkov V. I. Viktor Ivanovich, Sachkova A. S. Anna Sergeevna
Summary:	Title screen “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%.
Language:	English
Published:	2020
Subjects:	электронный ресурс труды учёных ТПУ malignant neoplasm classification electronic nose neural network metal oxide semiconductor sensor gas analyzer злокачественные новообразования нейронные сети газоанализаторы
Online Access:	https://doi.org/10.3390/diagnostics10090677
Format:	Electronic Book Chapter
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663483

Internet

https://doi.org/10.3390/diagnostics10090677