Neural networks for increased accuracy of allergenic pollen monitoring

Abstract Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this pr...

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Autores principales:	Marcel Polling, Chen Li, Lu Cao, Fons Verbeek, Letty A. de Weger, Jordina Belmonte, Concepción De Linares, Joost Willemse, Hugo de Boer, Barbara Gravendeel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4f1f306badec4094858a3e2ccc023952
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spelling	oai:doaj.org-article:4f1f306badec4094858a3e2ccc0239522021-12-02T15:57:20ZNeural networks for increased accuracy of allergenic pollen monitoring10.1038/s41598-021-90433-x2045-2322https://doaj.org/article/4f1f306badec4094858a3e2ccc0239522021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90433-xhttps://doaj.org/toc/2045-2322Abstract Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.Marcel PollingChen LiLu CaoFons VerbeekLetty A. de WegerJordina BelmonteConcepción De LinaresJoost WillemseHugo de BoerBarbara GravendeelNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marcel Polling Chen Li Lu Cao Fons Verbeek Letty A. de Weger Jordina Belmonte Concepción De Linares Joost Willemse Hugo de Boer Barbara Gravendeel Neural networks for increased accuracy of allergenic pollen monitoring
description	Abstract Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.
format	article
author	Marcel Polling Chen Li Lu Cao Fons Verbeek Letty A. de Weger Jordina Belmonte Concepción De Linares Joost Willemse Hugo de Boer Barbara Gravendeel
author_facet	Marcel Polling Chen Li Lu Cao Fons Verbeek Letty A. de Weger Jordina Belmonte Concepción De Linares Joost Willemse Hugo de Boer Barbara Gravendeel
author_sort	Marcel Polling
title	Neural networks for increased accuracy of allergenic pollen monitoring
title_short	Neural networks for increased accuracy of allergenic pollen monitoring
title_full	Neural networks for increased accuracy of allergenic pollen monitoring
title_fullStr	Neural networks for increased accuracy of allergenic pollen monitoring
title_full_unstemmed	Neural networks for increased accuracy of allergenic pollen monitoring
title_sort	neural networks for increased accuracy of allergenic pollen monitoring
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4f1f306badec4094858a3e2ccc023952
work_keys_str_mv	AT marcelpolling neuralnetworksforincreasedaccuracyofallergenicpollenmonitoring AT chenli neuralnetworksforincreasedaccuracyofallergenicpollenmonitoring AT lucao neuralnetworksforincreasedaccuracyofallergenicpollenmonitoring AT fonsverbeek neuralnetworksforincreasedaccuracyofallergenicpollenmonitoring AT lettyadeweger neuralnetworksforincreasedaccuracyofallergenicpollenmonitoring AT jordinabelmonte neuralnetworksforincreasedaccuracyofallergenicpollenmonitoring AT concepciondelinares neuralnetworksforincreasedaccuracyofallergenicpollenmonitoring AT joostwillemse neuralnetworksforincreasedaccuracyofallergenicpollenmonitoring AT hugodeboer neuralnetworksforincreasedaccuracyofallergenicpollenmonitoring AT barbaragravendeel neuralnetworksforincreasedaccuracyofallergenicpollenmonitoring
_version_	1718385328182525952

Neural networks for increased accuracy of allergenic pollen monitoring

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