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Título
A low-cost automated digital microscopy platform for automatic identification of diatoms
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Applied Sciences
Número de la revista
17
Cita Bibliográfica
Salido, J., Sánchez, C., Ruiz-Santaquiteria, J., Cristóbal, G., Blanco, S., & Bueno, G. (2020). A low-cost automated digital microscopy platform for automatic identification of diatoms. Applied Sciences, 10(17), Article e6033. https://doi.org/10.3390/APP10176033
Editorial
MDPI
Fecha
2020
Resumen
[EN] 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
Materia
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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