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Schaefer, J.* ; Milling, M.* ; Schuller, B.W.* ; Bauer, B.* ; Brunner, J.O.* ; Traidl-Hoffmann, C. ; Damialis, A.*

Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach.

Sci. Total Environ. 796:148932 (2021)
Postprint DOI PMC
Open Access Green
Allergic diseases have been the epidemic of the century among chronic diseases. Particularly for pollen allergies, and in the context of climate change, as airborne pollen seasons have been shifting earlier and abundances have been becoming higher, pollen monitoring plays an important role in generating high-risk allergy alerts. However, this task requires labour-intensive and time-consuming manual classification via optical microscopy. Even new-generation, automatic, monitoring devices require manual pollen labelling to increase accuracy and to advance to genuinely operational devices. Deep Learning-based models have the potential to increase the accuracy of automated pollen monitoring systems. In the current research, transfer learning-based convolutional neural networks were employed to classify pollen grains from microscopic images. Given a high imbalance in the dataset, we incorporated class weighted loss, focal loss and weight vector normalisation for class balancing as well as data augmentation and weight penalties for regularisation. Airborne pollen has been routinely recorded by a Bio-Aerosol Analyzer (BAA500, Hund GmbH) located in Augsburg, Germany. Here we utilised a database referring to manually classified airborne pollen images of the whole pollen diversity throughout an annual pollen season. By using the cropped pollen images collected by this device, we achieved an unweighted average F1 score of 93.8% across 15 classes and an unweighted average F1 score of 75.9% across 31 classes. The majority of taxa (9 of 15), being also the most abundant and allergenic, showed a recall of at least 95%, reaching up to a remarkable 100% in pollen from Taxus and Urticaceae. The recent introduction of novel pollen monitoring devices worldwide has pointed to the necessity for real-time, automatic measurements of airborne pollen and fungal spores. Thus, we may improve everyday clinical practice and achieve the most efficient prophylaxis of allergic patients.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Aerobiology ; Automatic Classification ; Convolutional Neural Network ; Machine Learning ; Pollen; Identification
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 0048-9697
e-ISSN 1879-1026
Zeitschrift Science of the Total Environment, The
Quellenangaben Band: 796, Heft: , Seiten: , Artikelnummer: 148932 Supplement: ,
Verlag Elsevier
Verlagsort Radarweg 29, 1043 Nx Amsterdam, Netherlands
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Environmental Medicine (IEM)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Allergy
PSP-Element(e) G-503400-001
Förderungen EU-COST Action ADOPT (New approaches in detection of pathogens and aeroallergens) (EU Framework Program Horizon 2020)
Helmholtz Climate Initiative (HI-CAM), Mitigation and Adaptation
Christine Kuhne-Center for Allergy Research and Education (CK-CARE)
DOI 10.1016/j.scitotenv.2021.148932
WOS ID WOS:000701140100009
Scopus ID 85110037311
PubMed ID 34273827
Erfassungsdatum 2021-08-04
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