Automatic Image Classification Using Neural Networks Increases Accuracy for Allergenic Pollen Monitoring
Abstract
Abstract Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen are 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 a Convolutional Neural Network (CNN) 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 have very low allergenic relevance, those from several species of Parietaria are severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these to train the CNN model VGG16. The model shows that Urticaceae pollen can be distinguished with 98.3% accuracy. We then apply our model on 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.
Authors 10
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Affiliation as printed
Naturalis Biodiversity Center, Leiden
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Affiliation as printed
Leiden Institute of Advanced Computer Science (LIACS), Leiden
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Affiliation as printed
Leiden Institute of Advanced Computer Science (LIACS), Leiden
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Affiliation as printed
Leiden Institute of Advanced Computer Science (LIACS), Leiden
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Leiden University Medical Center
Affiliation as printed
Department of Pulmonology, Leiden University Medical Center, Leiden
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Universitat Autònoma de Barcelona
Affiliation as printed
Institute of Environmental Sciences and Technology (ICTA-UAB), Universitat Autònoma de Barcelona, Bellaterra, Cerdanyola del Vallès
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Universitat Autònoma de Barcelona
Affiliation as printed
Institute of Environmental Sciences and Technology (ICTA-UAB), Universitat Autònoma de Barcelona, Bellaterra, Cerdanyola del Vallès
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Affiliation as printed
Microbial Sciences, Institute of Biology, Leiden
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Affiliation as printed
Natural History Museum, University of Oslo
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Affiliation as printed
Naturalis Biodiversity Center, Leiden
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