Classification of coralline algae using deep learning techniques on SEM images

The taxonomy of calcareous red algae continues to undergo revisions, especially since the spread of molecular techniques (Pezzolesi et al., 2019; Caragnano et al., 2021). Genetic analyses challenged the morphological approach to species identification, that relies on thallus organization, morphology of cells and reproductive structures observed with high magnification scanning electron microscopy (SEM), or in thin sections under optical microscope. Recently, a growing attention is also given to the calcified structures and to the shape of the crystallites composing the cell walls, which are diagnostic at the level of family (Auer and Piller, 2020). Looking for new identification tools therefore represents a current challenge, which could have crucial implications in paleontology. Convolutional neural networks (CNN) have successfully been applied for the classification of fish, bivalves, and foraminifera (de Lima et al., 2020). To the best of our knowledge, there is no literature about CNN for species identification using SEM images. We tested the potential of CNN to classify SEM images taken from different species of coralline algae commonly found in Mediterranean waters: Lithothamnion corallioides (P. Crouan & H. Crouan) P. Crouan & H. Crouan 1867, Mesophyllum philippii (Foslie) Adey 1970 and Lithophyllum racemus (Lamarck) Foslie 1901. The model provided promising results in terms of accuracy, considering the small set of images used (~40 per species). SEM acquisition, indeed, has significant costs that limit the number of images available for cross-validation. Further efforts should focus on enhancing the image dataset to improve the CNN classification capability and on the interpretation of the features determining the classes.