Citation: Chen, X.; Ju, F. Automatic
Classification of Pollen Grain
Microscope Images Using a
Multi-Scale Classifier with SRGAN
Deblurring. Appl. Sci. 2022, 12, 7126.
https://doi.org/10.3390/
app12147126
Academic Editor: Antonio
Fernández-Caballero
Received: 8 June 2022
Accepted: 12 July 2022
Published: 14 July 2022
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Article
Automatic Classification of Pollen Grain Microscope Images
Using a Multi-Scale Classifier with SRGAN Deblurring
Xingyu Chen and Fujiao Ju *
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
chenxingyu531@126.com
* Correspondence: jfj2017@bjut.edu.cn
Abstract:
Pollen allergies are seasonal epidemic diseases that are accompanied by high incidence
rates, especially in Beijing, China. With the development of deep learning, key progress has been
made in the task of automatic pollen grain classification, which could replace the time-consuming and
laborious manual identification process using a microscope. In China, few pioneering works have
made significant progress in automatic pollen grain classification. Therefore, we first constructed
a multi-class and large-scale pollen grain dataset for the Beijing area in preparation for the task of
pollen classification. Then, a deblurring pipeline was designed to enhance the quality of the pollen
grain images selectively. Moreover, as pollen grains vary greatly in size and shape, we proposed
an easy-to-implement and efficient multi-scale deep learning architecture. Our experimental results
showed that our architecture achieved a 97.7% accuracy, based on the Resnet-50 backbone network,
which proved that the proposed method could be applied successfully to the automatic identification
of pollen grains in Beijing.
Keywords: pollen classification; pollen image dataset; multi-scale classifier; deblurring
1. Introduction
Allergic diseases are common and are found in clinical practice [
1
]. They are listed as
one of the three major diseases that need to be prevented and controlled in the 21st century
by the World Health Organization. Pollen allergens are one of the main causes and affect
up to 30% of the population in industrialized countries [
2
]. Certain quantities of plant
pollen allergens in the air can induce a series of allergic diseases, such as allergic rhinitis,
bronchial asthma and dermatitis. These allergic diseases that are caused by plant pollen are
also called hay fever. With the intensified urbanization of human society and the expansion
of planting areas, the levels of pollen allergens have also increased. This has made pollen
allergies become seasonal epidemic diseases that are accompanied by high incidence rates.
In the United States, pollen allergies affect approximately 5% of the population and as much
as 15% of the population in some regions. In Europe, they affect 20% of the population and
it has been estimated that 30% of the population could be affected in the next 20 years [
3
].
In China, the incidence rate of pollen allergies is generally 0.5% to 1%, but it reaches 5%
in high incidence areas [
4
]. This percentage has been increasing over recent years as well.
Nearly 30% of allergic rhinitis cases in China are caused by pollen allergens. In Beijing, hay
fever patients account for about 1/3 of all respiratory allergy patients.
Effective pollen prediction can prompt hay fever patients to take positive counter-
measures. Therefore, it is of great practical significance to identify and count the main
allergenic pollen grains in the air. According to our survey, few pioneering works have
made significant progress in this topic in China at present. As an international metropolis,
the urban population in Beijing has shown an upward trend in pollen allergy incidence
rate over recent years. As a result, the proportion of the population with pollen allergies is
much higher than that in other cities. Considering this fact, we constructed an allergenic
Appl. Sci. 2022, 12, 7126. https://doi.org/10.3390/app12147126 https://www.mdpi.com/journal/applsci
