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Article

A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms

1
VISILAB, University of Castilla-La Mancha, Av. Camilo José Cela s/n, 13071 Ciudad Real, Spain
2
Institute of Optics, Spanish National Research Council (CSIC), Serrano 121, 28006 Madrid, Spain
3
Institute of the Environment, University of Leon, 24071 León, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(17), 6033; https://doi.org/10.3390/app10176033
Received: 28 July 2020 / Revised: 18 August 2020 / Accepted: 25 August 2020 / Published: 31 August 2020
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology Ⅱ)
Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatoms. View Full-Text
Keywords: applied deep learning; digital microscopy; diatom identification; diatom classification; microscope automation applied deep learning; digital microscopy; diatom identification; diatom classification; microscope automation
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MDPI and ACS Style

Salido, J.; Sánchez, C.; Ruiz-Santaquiteria, J.; Cristóbal, G.; Blanco, S.; Bueno, G. A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms. Appl. Sci. 2020, 10, 6033. https://doi.org/10.3390/app10176033

AMA Style

Salido J, Sánchez C, Ruiz-Santaquiteria J, Cristóbal G, Blanco S, Bueno G. A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms. Applied Sciences. 2020; 10(17):6033. https://doi.org/10.3390/app10176033

Chicago/Turabian Style

Salido, Jesús, Carlos Sánchez, Jesús Ruiz-Santaquiteria, Gabriel Cristóbal, Saul Blanco, and Gloria Bueno. 2020. "A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms" Applied Sciences 10, no. 17: 6033. https://doi.org/10.3390/app10176033

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