SavedAdd your journal

Journals / Middle East Journal of Science / 2025 / Volume: 11 Issue: 2

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

Gökçe Kendirlioğlu Şimşek(FIRAT ÜNİVERSİTESİ)0000-0001-8896-2893
Merve Ertarğın(MUNZUR ÜNİVERSİTESİ)0000-0003-4493-7260
Pages
—
Publication Date
2025-12-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data:The experiments were performed on a small and imbalanced dataset, where all models achieved an accuracy of 100%.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:RegNetY-008 · ConvNeXt-Tiny · ViT-Tiny

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.

🔖

Cite This Article

Şimşek, G. K., Ertarğın, M. (2025). A COMPARISON OF CNN AND TRANSFORMER-BASED MODELS FOR ALGAL SPECIES CLASSIFICATION. Middle East Journal of Science, Volume: 11 Issue: 2. https://doi.org/10.51477/mejs.1674736

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (33)

  1. Chew, K.W., Yap, J.Y., Show, P.L., Suan, N.H., Juan, J.C., Ling, T.C., Lee, D.J., Chang, J.S., “Microalgae biorefinery: high value products perspectives”, Bioresource Technology, 229, 53–62, 2017. https://doi.org/10.1016/j.biortech.2017.01.006.
  2. Dragone, G., Fernandes, B., Vicente, A., Teixeira, J., “Third generation biofuels from microalgae”, Current Research, Technology and Education Topics in Applied Microbiology and Microbial Biotechnology, 1, 135–143, 2010. https://doi.org/10.1016/j.apenergy.2011.03.012
  3. Chisti, Y., “Biodiesel from microalgae beats bioethanol”, Trends in Biotechnology, 26(3), 126–131, 2008. https://doi.org/10.1016/j.tibtech.2007.12.002.
  4. Bwapwa, J.K., Anandraj, A., Trois, C., “Possibilities for conversion of microalgae oil into aviation fuel: a review”, Renewable and Sustainable Energy Reviews, 80, 1345–1357, 2017. https://doi.org/10.1016/j.rser.2017.05.224.
  5. Benemann, J.R., “Hydrogen production by microalgae”, Journal of Applied Phycology, 12(3–5), 291–300, 2000. https://doi.org/10.1023/A:1008111715643.
  6. Beneroso, D., Bermúdez, J.M., Arenillas, A., Menéndez, J.A., “Microwave pyrolysis of microalgae for high syngas production”, Bioresource Technology, 144, 240–246, 2013. https://doi.org/10.1016/j.biortech.2013.06.102.
  7. Khan, M.I., Shin, J.H., Kim, J.D., “The promising future of microalgae: Current status, challenges, and optimization of a sustainable and renewable industry for biofuels, feed, and other products”, Microbial Cell Factories, 17(1), 36, 2018. https://doi.org/10.1186/s12934-018-0879-x.
  8. Fawley, M.W., Fawley, K.P., “Identification of eukaryotic microalgal strains”, Journal of Applied Phycology, 32(5), 2699–2709, 2020. https://doi.org/10.1007/s10811-020-02190-5.
  9. Darienko, T., Gustavs, L., Eggert, A., Wolf, W., Pröschold, T., “Evaluating the species boundaries of green microalgae (Coccomyxa, Trebouxiophyceae, Chlorophyta) using integrative taxonomy and DNA barcoding with further implications for the species identification in environmental samples”, Plos one, 10(6), e0127838, 2015. https://doi.org/10.1371/journal.pone.0127838.
  10. Guiry, M.D., “How many species of algae are there?”, Journal of Phycology, 48, 1057–1063, 2012.
  11. Gumz, W., Gas Producers and Blast Furnaces, New York, John Wiley and Sons Inc., 2008.
  12. Zhang, J., Li, C., Rahaman, M.M., Yao, Y., Ma, P., Zhang, J., Zhao, X., Jiang, T., Grzegorzek, M., “A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches”, Artificial Intelligence Review, 1–70, 2021.
  13. Chong, J.W.R., Khoo, K.S., Chew, K.W., Ting, H.-Y., Show, P.L., “Trends in digital image processing of isolated microalgae by incorporating classification algorithm”, Biotechnology Advances, 63, 108095, 2023. https://doi.org/10.1016/j.biotechadv.2023.108095.
  14. Sonmez, M.E., Altinsoy, B., Ozturk, B.Y., Gumus, N.E., Eczacioglu, N.,“Deep learning-based classification of microalgae using light and scanning electron microscopy images”, Micron, 172, 103506, 2023. https://doi.org/10.1016/j.micron.2023.103506.
  15. Yamada, T., Cyanobacteria and Algae/Recombinant Microbes for Industrial and Agricultural Applications, Marcel Dekker Inc., 701–712, 1994.
  16. Richmond, A., “Outdoor mass cultures of microalgae”, in Richmond, A. (Ed.), Handbook of Microalgal Mass Cultures, CRC Press Inc., Boca Raton, Florida, pp. 285–329, 1988.
  17. Vonshak, A., “Outdoor Mass Production of Spirulina: The Basic Concept”, in: Spirulina platensis (Arthrospira): Physiology, Cell Biology and Biotechnology (Ed. A. Vonshak), Taylor & Francis Ltd., 1997, pp. 79–99.
  18. Eitinger, T., Schlegel, H.G., Allgemeine Mikrobiologie, Georg Thieme Verlag, 2007.
  19. Brown, M.R., Mular, M., Miller, I., Blackburn, S.I., “The vitamin content of microalgae used in aquaculture”, Aquaculture, 306, 10–15, 2019.
  20. Shah, M.R., Liang, Y., Cheng, J.J., Daroch, M., “Astaxanthin-producing green microalgae: Advances and prospects”, Critical Reviews in Biotechnology, 41(4), 465–487, 2021.
  21. García, J.L., de Vicente, M., Galán, B., “Microalgae, old sustainable food and fashion nutraceuticals”, Microbial Biotechnology, 13(1), 100–109, 2020.
  22. Otálora, P., Guzmán, J.L., Acién, F.G., Berenguel, M., Reul, A., “Microalgae classification based on machine learning techniques”, Algal Research, 55, 102256, 2021.https://doi.org/10.1016/j.algal.2021.102256.
  23. Drews, P., Colares, R.G., Machado, P., de Faria, M., Detoni, A., Tavano, V., “Microalgae classification using semi-supervised and active learning based on Gaussian mixture models”, Journal of the Brazilian Computer Society, 19, 411–422, 2013. https://doi.org/10.1007/s13173-013-0121-y.
  24. Harmon, J., Mikami, H., Kanno, H., Ito, T., Goda, K.,“Accurate classification of microalgae by intelligent frequency-division-multiplexed fluorescence imaging flow cytometry”, OSA Continuum, 3, 430, 2020. https://doi.org/10.1364/osac.387523.
  25. Kaya, V., Akgül, I., Tanır, Ö., “A novel hybrid model based on machine and deep learning techniques for the classification of microalgae”, Phyton, 92(9), 2519, 2023.
  26. Liu, J.Y., Zeng, L.H., Ren, Z.H., Du, T.M., Liu, X.,“Rapid in situ measurements of algal cell concentrations using an artificial neural network and single-excitation fluorescence spectrometry”, Algal Research, 45, 101739, 2020.https://doi.org/10.1016/j.algal.2019.101739.
  27. Zarrouk, C., “Contribution à l’étude d’une cyanobactérie: influence de divers facteurs physiques et chimiques sur la croissance et la photosynthèse de Spirulina maxima (Setchell et Gardner) Geitler”, Ph.D. thesis, University of Paris, Paris, France, 1966.
  28. Guillard, R.R.L., Ryther, J.H., “Studies of marine planktonic diatoms. I. Cyclotella nana Hustedt and Detonula confervaceae (Cleve) Gran”, Canadian Journal of Microbiology, 8, 229–239, 1962.
  29. Allen, M.M., Stanier, R.Y., “Growth and Division of Some Unicellular Blue-green Algae”, Journal of General Microbiology, 51(2), 199–202, 1968.
  30. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.,“An image is worth 16×16 words: Transformers for image recognition at scale”, arXiv preprint arXiv:2010.11929, 2020.
  31. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Polosukhin, I., “Attention is all you need”, Advances in Neural Information Processing Systems, 30, 2017.
  32. Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Dollár, P., “Designing network design spaces”, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10428–10436, 2020.
  33. Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S., “A convnet for the 2020s”, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11976–11986, 2022.