Fish Species Classification with Deep Learning and Bayesian Optimization: Effectiveness and Comparative Results

Authors : Hüseyin Aydilek, Mustafa Yasin Erten

Pages : 92-107

Doi:10.29132/ijpas.1637721

View : 44 | Download : 32

Publication Date : 2025-06-30

Article Type : Research Paper

Abstract :This study examines the effectiveness of deep learning-based models in the classification and monitoring of fish species. A dataset obtained from the Kaggle platform, containing 31 different fish species, was used to train MobileNetV2, DenseNet121, and VGG19 models. Bayesian optimization was applied to enhance model performance and determine the optimal hyperparameters. The results indicate that models trained with Bayesian optimization achieved significantly higher accuracy compared to those trained with randomly assigned hyperparameters. Additionally, the ensemble learning approach, which combined the outputs of individual models, yielded the best classification performance. This study demonstrates that deep learning techniques serve as a crucial tool for marine ecosystem conservation and sustainable fisheries practices.
Keywords : Derin öğrenme, Balık türü sınıflandırma, MobilNetV2, VGG19, DenseNet121, Bayes optimizasyonu.

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