Splitting train-validation-test of data.

Aerobiology is a branch of biology that studies microorganisms passively transferred by the air. Bacteria, viruses, fungal spores, tiny insects, and pollen grains are samples of microorganisms. Pollen grains classification is essential in medicine, agronomy, economy, etc. It is performed traditionally (manually) and automatically. The automated approach is faster, more accurate, cost-effective, and with less human intervention than the manual method. In this paper, we introduce a Residual Cognitive Attention Network (RCANet) for the automated classification of pollen grains microscopic images. The suggested attention block, Ventral-Dorsal Ateetntion Block (VDAB), is designed based on the ventral (temporal) and dorsal (parietal) pathways of the occipital lobe. It is embedded in each Basic Block of the architecture of ResNet18. The VDAB is composed of ventral and dorsal attention blocks. The ventral and dorsal streams detect the structure and location of the pollen grain, respectively. According to the mentioned pathways, the Ventral Attention Block (VAB) extracts the channels related to the shape of the pollen grain, and the Dorsal Attention Block (DAB) is focused on its position. Three publicly pollen grains datasets including the Cretan Pollen Dataset (CPD), Pollen13K, and Pollen23E are employed for experiments. The ResNet18 and the proposed method (RCANet) are trained on the datasets and the proposed RCANet obtained higher performance metrics than the ResNet18 in the test step. It achieved weighted F1-score values of 98.69%, 97.83%, and 98.24% with CPD, Pollen13K, and Pollen23E datasets, respectively.