Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

被引：6
作者：
Chernov, Vladimir I. [1 ]
Choynzonov, Evgeniy L. [1 ]
Kulbakin, Denis E. [1 ]
Obkhodskaya, Elena V. [2 ]
Obkhodskiy, Artem V. [2 ,3 ]
Popov, Aleksandr S. [2 ,3 ]
Sachkov, Victor I. [2 ]
Sachkova, Anna S. [3 ]
机构：
[1] Russian Acad Sci, Canc Res Inst, Tomsk Natl Res Med Ctr, 5 Kooperativny St, Tomsk 634009, Russia
[2] Natl Res Tomsk State Univ, Chem Technol Lab, 36 Lenin Ave, Tomsk 634050, Russia
[3] Natl Res Tomsk Polytech Univ, Sch Nucl Sci & Engn, 30 Lenin Ave, Tomsk 634050, Russia
来源：
DIAGNOSTICS | 2020年 / 10卷 / 09期
关键词：
malignant neoplasm; classification; electronic nose; neural network; metal oxide semiconductor sensor; gas analyzer; VOLATILE ORGANIC-COMPOUNDS; LUNG-CANCER; ELECTRONIC NOSE; POTENTIAL MARKERS; BREATH SAMPLES; SURGERY; HEAD;
D O I：
10.3390/diagnostics10090677
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
"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%.
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页数：11
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