Appl. Sci. 2017, 7(5), 460; doi:10.3390/app7050460
Automated Diatom Classification (Part B): A Deep Learning Approach
Anibal Pedraza 1
, Gloria Bueno 1,*
, Oscar Deniz 1, Gabriel Cristóbal 2, Saúl Blanco 3 and María Borrego-Ramos 3
, Gloria Bueno 1,*
, Oscar Deniz 1, Gabriel Cristóbal 2, Saúl Blanco 3 and María Borrego-Ramos 31
VISILAB Research Group, University of Castilla La Mancha, Av. Camilo José Cela s/n, Ciudad Real 13071, Spain
2
Institute of Optics, Spanish National Research Council (CSIC), Serrano 121, Madrid 8006, Spain
3
The Institute of the Environment, University of Leon, León E-24071, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Giacomo Cuttone
Received: 8 March 2017 / Revised: 21 April 2017 / Accepted: 21 April 2017 / Published: 2 May 2017
(This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images)
Abstract
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. View Full-TextKeywords:
convolutional neural networks; deep learning; classification; segmentation; normalization; image acquisition; diatoms
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Pedraza, A.; Bueno, G.; Deniz, O.; Cristóbal, G.; Blanco, S.; Borrego-Ramos, M. Automated Diatom Classification (Part B): A Deep Learning Approach. Appl. Sci. 2017, 7, 460.
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