Abstract
Certain phytoplankton species can produce potent toxins that can raise health concerns, especially if these species proliferate in water sources. Furthermore, phytoplankton can drastically and rapidly multiply their population in water, increasing the possibility of dangerous contamination. Nowadays, preventive water analyses related to phytoplankton are routinely and manually performed by the experts. These methods have major limitations in terms of reliability and repeatability as well as throughput due to their complexity and length. Therefore, the automatization of these tasks is particularly desirable to lower the workload of the experts and ease the whole process of potability analysis. Previous state of the art works can segment and classify phytoplankton from conventional microscopy images of multiple specimens. However, they require classical image features, which need ad-hoc feature engineering, a complex and lengthy process. Thus, employing novel deep learning-based deep features is highly desirable as it would improve the flexibility of the methods. In this manuscript, we present a study regarding the performance of different pre-trained deep neural networks for the extraction of deep features in order to identify and classify phytoplankton. The experimental results are satisfactory as we improve the performance of the state of the art approaches. Furthermore, as we eliminate the need for classical image features, we improve the adaptability of the methods.
This research was funded by Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the predoctoral grants contract ref. ED481A 2021/147 and ref. ED481A 2021/140 and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%).
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Rivas-Villar, D., Morano, J., Rouco, J., Penedo, M.G., Novo, J. (2022). Deep Features-Based Approaches for Phytoplankton Classification in Microscopy Images. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2022. EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham. https://doi.org/10.1007/978-3-031-25312-6_49
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