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Automated classification of pollen grains microscopic images using cognitive attention based on human Two Visual
Mohammad Zolfaghari1, Hedieh Sajedi2
1Department of Computer Science, University of Tehran, Kish International Campus, Kish, Iran.
Plos One
|November 21, 2024
Summary
A new Residual Cognitive Attention Network (RCANet) accurately classifies pollen grains from microscopic images. This automated method, inspired by human visual pathways, surpasses traditional techniques for aerobiology applications.
Area of Science:
- Aerobiology: The study of airborne microorganisms and their environmental impact.
- Computational Biology: Application of computational approaches to biological data analysis.
Background:
- Accurate classification of pollen grains is crucial for medicine, agronomy, and economic sectors.
- Traditional manual pollen classification is time-consuming and prone to human error.
- Automated methods offer increased speed, accuracy, and efficiency in pollen analysis.
Purpose of the Study:
- To introduce a novel Residual Cognitive Attention Network (RCANet) for automated pollen grain classification.
- To develop an attention mechanism inspired by human visual processing for enhanced feature extraction.
- To evaluate the performance of RCANet against existing methods on public pollen datasets.
Main Methods:
- Development of a Residual Cognitive Attention Network (RCANet) incorporating a Ventral-Dorsal Attention Block (VDAB).
- The VDAB is designed based on the ventral and dorsal visual pathways, with separate modules for shape (Ventral Attention Block) and location (Dorsal Attention Block) detection.
- Integration of the VDAB into the ResNet18 architecture for microscopic pollen image classification.
Main Results:
- RCANet achieved superior performance compared to the standard ResNet18 on three public pollen datasets (CPD, Pollen13K, Pollen23E).
- The proposed method demonstrated high accuracy, achieving weighted F1-scores of 98.69% (CPD), 97.83% (Pollen13K), and 98.24% (Pollen23E).
- The VDAB effectively extracts relevant features for pollen classification by processing shape and location information.
Conclusions:
- RCANet provides a highly accurate and efficient automated solution for pollen grain classification from microscopic images.
- The VDAB, inspired by human visual cognition, significantly enhances the performance of deep learning models in this domain.
- This automated approach holds promise for advancing aerobiology research and related applications.
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