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

Research output: Contribution to journalArticlepeer-review

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%.

Original languageEnglish
Article number677
JournalDiagnostics
Volume10
Issue number9
DOIs
Publication statusPublished - Sep 2020

Keywords

  • Classification
  • Electronic nose
  • Gas analyzer
  • Malignant neoplasm
  • Metal oxide semiconductor sensor
  • Neural network

ASJC Scopus subject areas

  • Clinical Biochemistry

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