RT info:eu-repo/semantics/article
T1 Automated Diatom Classification (Part B): A Deep Learning Approach
A1 Pedraza, Aníbal
A1 Bueno García, María Gloria
A1 Déniz Suárez, Óscar
A1 Cristóbal Pérez, Gabriel
A1 Blanco Lanza, Saúl
A1 Borrego Ramos, María
A2 Ecologia
K1 Biología
K1 Biotecnología
K1 Ecología. Medio ambiente
K1 Convolutional neural networks
K1 Deep learning;
K1 Classification
K1 Segmentation
K1 Normalization
K1 Image acquisition
K1 Diatoms
K1 2417.07 Algología (Ficología)
K1 2417.20 Taxonomía Vegetal
AB [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
PB MDPI
LK https://hdl.handle.net/10612/19283
UL https://hdl.handle.net/10612/19283
NO 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
NO This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 15-may-2024