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Título
Automated Diatom Classification (Part B): A Deep Learning Approach
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Applied Sciences
Número de la revista
5
Cita Bibliográfica
Pedraza, A., Bueno, G., Deniz, O., Cristóbal, G., Blanco, S., & Borrego-Ramos, M. (2017). Automated diatom classification (Part B): a deep learning approach. Applied Sciences, 7(5) Article e460. https://doi.org/10.3390/APP7050460
Editorial
MDPI
Fecha
2017
Resumen
[EN] Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN) are becoming an extensively used technique for image classification in a variety of problems. This paper approaches diatom classification with this technique, in order to demonstrate whether it is suitable for solving the classification problem. An extensive dataset was specifically collected (80 types, 100 samples/type) for this study. The dataset covers different illumination conditions and it was computationally augmented to more than 160,000 samples. After that, CNNs were applied over datasets pre-processed with different image processing techniques. An overall accuracy of 99% is obtained for the 80-class problem and different kinds of images (brightfield, normalized). Results were compared to previous presented classification techniques with different number of samples. As far as the authors know, this is the first time that CNNs are applied to diatom classification
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Palabras clave
Peer review
SI
ID proyecto
- Info:eu-repo/grantAgreement/MINECO/Programa Estatal de I+D+I Orientada a los Retos de la Sociedad/CTM2014-51907-C2-R/ES/Desarrollo de métodos automáticos de identificación de diatomeas en el análisis cuantitativo y monitorización de la calidad de agua/AQUALITAS
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