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Diatom Classification Including Morphological Adaptations Using CNNs

AuthorsSánchez, Carlos ; Vallez, Noelia; Bueno, Gloria; Cristóbal, Gabriel
Issue Date1-Jul-2019
Citation9th Iberian Conference on Pattern Recognition and Image Analysis (2019)
AbstractAbstract. Diatoms are a major group of aquatic microalgae. They are widely used in different fields such as environmental studies to estimate water quality. This paper presents the use of convolutional neural net- works (CNNs) to identify diatoms during their life cycle. This life cycle involves morphological and other changes to the diatom frustule adding intraclass variance and making harder the classification task. The per- formance of CNNs is compared against a classical image classification scheme (i.e., feature extraction and classification) using a 14 classes dataset with a total number of 1085 images ranging from 40 to 120 images per class. Classification accuracy was 99.07% and 99.7% for CNNs and classical methods respectively.
DescriptionIbPRIA 2019, Madrid, Spain, 1-4 July 2019
Publisher version (URL)http://dx.doi.org/10.1007/978-3-030-31332-6_28
Identifiersdoi: 10.1007/978-3-030-31332-6_28
Appears in Collections:(CFMAC-IO) Comunicaciones congresos
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