Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks

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.

Precise automatic classification of 46 different pollen types with convolutional neural networks

“…Previous approaches to automated palynology are comprehensively summarized in [9] and [10]. They can be divided into image-based and non-image based methods.…”

“…Flenley [8] was the first to call attention to the need and potential of automation of pollen counting. A handful of early attempts were published in the later decades of the 20th century, but the rapid increase in capability in computational intelligence over the early part of the 21st century resulted in considerable acceleration in the field during this time, with numerous attempts at partial or complete automation of palynology appearing in the literature, summarized in [9] and [10].…”

“…Recently, Sevillano and Aznarte in [10] presented an example of pollen classification which applied deep learning convolutional neural networks to the POLEN23E image dataset, a freely available set of 805 bright-field microscope images of 23 different pollen types from the Brazilian savannah [12]. Their model achieved accuracies of over 97%.…”

3-Step flow focusing enables multidirectional imaging of bioparticles for imaging flow cytometry

“…Recently, publications have begun to address this issue, often making use of machine learning. 24,[27][28][29][30][31] In their work from 2014 Holt and Bennett give a profound overview on the principles and methods for automated palynology 32 and highlight the specific application-related requirements for automation, such as on-site analysis for airborne pollen, the ability to deal with lots of debris and damaged pollen grains for paleoecology.…”

Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

“…Even though there is extensive literature about computational intelligence techniques applied to pollen time series, such as random forests [7,12,23,24], artificial neural networks [9,10], and deep neural architectures [25], very few works have applied convolutional neural networks to time series. Nonetheless, CNNs have been extensively used in identifying and classifying pollen grains [26,27].…”