Abstract
In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Its complete automatization would save a high quantity of resources and provide valuable improvements especially for allergy-related information systems, but also for other application fields as paleoclimate reconstruction, quality control of honey based products, collection of evidences in criminal investigations or fabric dating and tracking. This paper presents three state-of-the-art deep learning classification methods applied to the recently published POLEN23E image dataset. The three methods make use of convolutional neural networks: the first one is strictly based on the idea of transfer learning, the second one is based on feature extraction and the third one represents a hybrid approach, combining transfer learning and feature extraction. The results from the three methods are indeed very good, reaching over 97% correct classification rates in images not previously seen by the models, where other authors reported around 70.
Highlights
The surge in the prevalence of allergies, with 30 percent of adults and 40 percent of children having at least one allergy according to the ACAAI [1], implies that measuring the concentrations of airborne pollen grains becomes a necessity for public health institutions and patients
In this paper, we present three automatic approaches based on deep learning that significantly increase the percentage of correct classifications on the same image dataset
In [6] the authors presented an image dataset, called POLEN23E, which consists of photos of 23 pollen types present in the Brazilian savannah: Anadenanthera colubrina, Arecaceae, Arrabidaea, Cecropia pachystachya, Chromolaena laevigata, Combretum discolor, Croton urucurana, Dipteryx alata, Eucalyptus, Faramea, Hyptis, Mabea fistulifera, Matayba guianensis, Mimosa somnians, Myrcia, Protium heptaphyllum, Qualea multiflora, Schinus terebinthifolius, Senegalia plumosa, Serjania laruotteana, Syagrus, Tridax procumbens and Urochloa decumbens
Summary
The surge in the prevalence of allergies, with 30 percent of adults and 40 percent of children having at least one allergy according to the ACAAI [1], implies that measuring the concentrations of airborne pollen grains becomes a necessity for public health institutions and patients. The determination of such concentrations is usually performed through volumetric spore traps which capture pollen grains, which in turn have to be visually identified and counted. The morphological similarity between pollen grains from different vegetal species complicates the identification process, which is usually performed by the visual inspection of optical microscope images by an experienced human operator.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
