%0 Journal Article
	%A Endrick Barnacin and  Jean-Luc Henry and  Jimmy Nagau and  Jack Molinié
	%D 2023
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 199, 2023
	%T Comparison of Machine Learning and Deep Learning Algorithms for Automatic Classification of 80 Different Pollen Species
	%U https://publications.waset.org/pdf/10013192
	%V 199
	%X Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. In this context, the automation of this task is urgent. In this work, we compare classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN and Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches.
	%P 440 - 444