A COMPARISON OF CNN AND TRANSFORMER-BASED MODELS FOR ALGAL SPECIES CLASSIFICATION

Year 2025, Volume: 11 Issue: 2, 155 - 169, 30.12.2025

Gökçe Kendirlioğlu Şimşek , Merve Ertarğın

https://doi.org/10.51477/mejs.1674736

Abstract

In this study, the classification of Spirulina platensis, Tetraselmis suecica, and Porphyridium cruentum algal species was conducted using RegNetY-008, ConvNeXt-Tiny, and ViT-Tiny models. The experiments were performed on a small and imbalanced dataset, where all models achieved an accuracy of 100%. To assess model performance, training and validation losses were analyzed, confirming the absence of overfitting. Furthermore, Grad-CAM was applied to visualize the decision-making processes of the models. The results indicate that although all models demonstrated high accuracy, the ViT-Tiny model exhibited the most interpretable visualizations by effectively focusing on algal cells. This study highlights the capability of transformer-based models to achieve high performance even on small and imbalanced datasets, offering a promising alternative for biological image analysis.

Keywords

RegNetY-008 , ConvNeXt-Tiny , ViT-Tiny , algae , classification

Ethical Statement

The author declares that this document does not require ethics committee approval or any special permission.

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