Analysis of 3D Urticaceae Pollen Classification Using Deep Learning Models
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
Due to the climate change, hay fever becomes a pressing healthcare problem
with an increasing number of affected population, prolonged period of affect
and severer symptoms. A precise pollen classification could help monitor the
trend of allergic pollen in the air throughout the year and guide preventive
strategies launched by municipalities. Most of the pollen classification works
use 2D microscopy image or 2D projection derived from 3D image datasets. In
this paper, we aim at using whole stack of 3D images for the classification and
evaluating the classification performance with different deep learning models.
The 3D image dataset used in this paper is from Urticaceae family, particularly
the genera Urtica and Parietaria, which are morphologically similar yet differ
significantly in allergenic potential. The pre-trained ResNet3D model, using
optimal layer selection and extended epochs, achieved the best performance with
an F1-score of 98.3%.
