Machine learning applications for spectral analysis of human exhaled breath for early diagnosis of diseases
| Autor: | Igor S. Golyak, Andrey N. Morozov, Elizaveta R. Kareva, Igor L. Fufurin, Pavel P. Demkin, Anastasiya S. Tabalina, Dmitriy R. Anfimov |
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| Rok vydání: | 2020 |
| Předmět: |
Physics
Neutral network Biomedical spectroscopy Artificial neural network business.industry Machine learning computer.software_genre Convolutional neural network law.invention law Principal component analysis Spectral analysis Artificial intelligence Quantum cascade laser business computer Optical path length |
| Zdroj: | Optics in Health Care and Biomedical Optics X. |
| DOI: | 10.1117/12.2584043 |
| Popis: | In this work, the possibility of using machine learning in the spectral analysis of exhaled breath for early diagnosis of diseases is considered. Experimental setup consists of a quantum cascade laser with a tuning range of 5.4–12.8 μm and Herriot astigmatic gas cell. A shallow convolutional neutral network and principal component analysis is used to identify biomarkers and its mixtures. A minimum detectable concentration for acetone and ethanol at sub-ppm level is obtained for optical path length up to 6 m and signal-to-noise less than 3. It is shown that neural networks in comparison with statistical methods give a lower detection limits for the same signal-to-noise ratio in the measured spectrum. |
| Databáze: | OpenAIRE |
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