Lung Cancer Screening Based on Type-different Sensor Arrays

Scientific Reports
Wang LiXititan Pi

Abstract

In recent years, electronic nose (e-nose) systems have become a focus method for diagnosing pulmonary diseases such as lung cancer. However, principles and patterns of sensor responses in traditional e-nose systems are relatively homogeneous. Less study has been focused on type-different sensor arrays. In this paper, we designed a miniature e-nose system using 14 gas sensors of four types and its subsequent analysis of 52 breath samples. To investigate the performance of this system in identifying and distinguishing lung cancer from other respiratory diseases and healthy controls, five feature extraction algorithms and two classifiers were adopted. Lastly, the influence of type-different sensors on the identification ability of e-nose systems was analyzed. Results indicate that when using the LDA fuzzy 5-NN classification method, the sensitivity, specificity and accuracy of discriminating lung cancer patients from healthy controls with e-nose systems are 91.58%, 91.72% and 91.59%, respectively. Our findings also suggest that type-different sensors could significantly increase the diagnostic accuracy of e-nose systems. These results showed e-nose system proposed in this study was potentially practicable in lung cancer screening ...Continue Reading

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Citations

Feb 13, 2019·Journal of Breath Research·Davide MarzoratiPietro Cerveri
Jun 20, 2019·Sensors·Henike Guilherme Jordan VossSergio Luiz Stevan
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Methods Mentioned

BETA
feature extractions
chip
PCA
feature extraction
feature

Software Mentioned

mySQL
LDA
LE

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