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

Open Access

Peer-reviewed

Research Article

About the Authors

Víctor Sevillano

Roles Investigation, Methodology, Software, Visualization, Writing – original draft

Affiliation Technical Superior School of Computer Engineering, Universidad Nacional de Educación a Distancia – UNED, Madrid, Spain

José L. Aznarte

Roles Conceptualization, Investigation, Methodology, Resources, Supervision, Validation, Writing – review & editing

Affiliation Artificial Intelligence Department, Universidad Nacional de Educación a Distancia – UNED, Madrid, Spain

The authors have declared that no competing interests exist.

Pollen
Is the Subject Area "Pollen" applicable to this article?

Thanks for your feedback.
Machine learning
Is the Subject Area "Machine learning" applicable to this article?

Thanks for your feedback.
Neural networks
Is the Subject Area "Neural networks" applicable to this article?

Thanks for your feedback.
Deep learning
Is the Subject Area "Deep learning" applicable to this article?

Thanks for your feedback.
Imaging techniques
Is the Subject Area "Imaging techniques" applicable to this article?

Thanks for your feedback.
Machine learning algorithms
Is the Subject Area "Machine learning algorithms" applicable to this article?

Thanks for your feedback.
Allergies
Is the Subject Area "Allergies" applicable to this article?

Thanks for your feedback.
Human learning
Is the Subject Area "Human learning" applicable to this article?

Thanks for your feedback.