RT info:eu-repo/semantics/article
T1 A low-cost automated digital microscopy platform for automatic identification of diatoms
A1 Salido Tercero, Jesús
A1 Sánchez Bueno, Carlos
A1 Ruiz-Santaquiteria Alegre, Jesús
A1 Cristóbal Pérez, Gabriel
A1 Blanco Lanza, Saúl
A1 Bueno García, María Gloria
A2 Ecologia
K1 Botánica
K1 Ecología. Medio ambiente
K1 Applied deep learning
K1 Digital microscopy
K1 Diatom identification
K1 Diatom classification
K1 Microscope automation
K1 2417.07 Algología (Ficología)
K1 2417.20 Taxonomía Vegetal
K1 2203.04 Microscopia Electrónica
K1 1203.02 Lenguajes Algorítmicos
K1 1203.12 Bancos de Datos
K1 1203.04 Inteligencia artificial
AB [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
PB MDPI
LK https://hdl.handle.net/10612/19567
UL https://hdl.handle.net/10612/19567
NO 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
NO This article belongs to the Special Issue Advanced Intelligent Imaging Technology Ⅱ
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 21-may-2024