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oai:munzur-rdlopen.rawdatalibrary.net:munzur/60012026-10-28T15:32:13Zopenairedrivercom_munzur_1col_munzur_2

A comparison of cnn and transformer-based models for algal species classificationErtarğın, MerveŞimşek, Gökçe KendirlioğluBiyolojiBilgisayar Bilimleri, Yapay ZekaIn 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.2025Articlehttps://rdlopen.rawdatalibrary.net/r/munzur/6001https://doi.org/10.51477/mejs.1674736enMiddle East Journal of Scienceinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/6001