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Automated classification of airborne pollen using neural networks.
Conf. Proc. IEEE Eng. Med. Biol. Soc., 4474-4478 (2019)
Pollen allergies are considered as a global epidemic nowadays, as they influence more than a quarter of the worldwide population, with this percentage expected to rapidly increase because of ongoing climate change. To date, alerts on high-risk allergenic pollen exposure have been provided only via forecasting models and conventional monitoring methods that are laborious. The aim of this study is to develop and evaluate our own pollen classification model based on deep neural networks. Airborne allergenic pollen have been monitored in Augsburg, Bavaria, Germany, since 2015, using a novel automatic Bio-Aerosol Analyzer (BAA 500, Hund GmbH). The automatic classification system is compared and evaluated against our own, newly developed algorithm. Our model achieves an unweighted average precision of 83.0 % and an unweighted average recall of 77.1 % across 15 classes of pollen taxa. Automatic, real-time information on concentrations of airborne allergenic pollen will significantly contribute to the implementation of timely, personalized management of allergies in the future. It is already clear that new methods and sophisticated models have to be developed so as to successfully switch to novel operational pollen monitoring techniques serving the above need.
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Publication type
Article: Journal article
Document type
Scientific Article
Language
Publication Year
2019
HGF-reported in Year
2019
ISSN (print) / ISBN
1557-170X
Conference Title
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Conference Date
23-27 July 2019
Conference Location
Berlin
Quellenangaben
Pages: 4474-4478
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Reviewing status
Peer reviewed
Institute(s)
Institute of Environmental Medicine (IEM)
POF-Topic(s)
30202 - Environmental Health
Research field(s)
Allergy
PSP Element(s)
G-503400-001
Scopus ID
85077860085
Erfassungsdatum
2020-01-28