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bioRxiv · 10.1101/2025.02.10.635050

DiatomNet: An automatic Diatom genus identification system through microscopic images and Deep Learning

Tabik, S.·Villar, P.·Casado, J.·Fernandez, D.·Sanchez, P.
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Abstract

Diatoms are microscopic organisms belonging to the algae kingdom. They adapt to the ecosystem and modify their shape and texture depending on hundreds of ecosystem variables. Hence, these micro-organism are considered as the most accurate indicator to measure water quality. Commonly, the recognition of the class of diatoms in a microscopic image has always been done by expert biologists knowledgeable about the morphometric characteristics of these organisms. This work proposes a new automatic diatom genus recognition system from microscopic images using state-of-the-art deep CNNs. In particular, 1) we developed a public high quality database organized into 44 genus-level diatom classes, 2) designed a robust diatom classification model, and 3) provided a user-friendly interface to utilize our automatic diatom recognition tool.

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Tabik, S., Villar, P., Casado, J., Fernandez, D., Sanchez, P.. 2025-02-11. DiatomNet: An automatic Diatom genus identification system through microscopic images and Deep Learning. https://doi.org/10.1101/2025.02.10.635050

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