Discriminating Artificial Cancer Breath Using an Electronic Nose: K-Nearest Neighbors Versus Long-Short Term Memory Network

Discriminating Artificial Cancer Breath Using an Electronic Nose: K-Nearest Neighbors Versus Long-Short Term Memory Network

This paper presents the use of k-NN for the classification of healthy human breath with or without the addition of lung cancer biomarkers. 236 breath samples collected from 17 persons over four months were analyzed by a custom electronic nose using commercial metal oxide sensors. About 90% of the sa...

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Bibliographic Details
Published in:2024 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) pp. 1 - 3
Main Authors: Martin, Justin, Falzone, Claudia, Romain, Anne-Claude
Format: Conference Proceeding
Language:English
Published: IEEE 12-05-2024
Subjects:
Biological system modeling
Biomarkers
Breath analysis
cancer
Clinical trials
e-nose
Electronic noses
kNN
LSTM
Lung cancer
metal oxide
Metals
Neural networks
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Summary:This paper presents the use of k-NN for the classification of healthy human breath with or without the addition of lung cancer biomarkers. 236 breath samples collected from 17 persons over four months were analyzed by a custom electronic nose using commercial metal oxide sensors. About 90% of the samples were correctly classed by the model. Long-Short Term Memory Neural Network could show promising results in this task as well and are under investigation.
DOI:10.1109/ISOEN61239.2024.10555978