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    Age recognition of a semi-pelagic fish (Carangiformes: Carangidae) using a Swin Transformer and Gaussian Process Classifier with otolith images
    (Elsevier, 2026) Turkoglu, Muammer; Durrani, Omerhan; Polat, Onur; Bal, Habib; Atessahin, Tuncay; Isguzar, Seda; Seyhan, Kadir
    Accurate age determination of commercially important fish species is essential for sustainable fisheries management and stock assessment. However, traditional methods relying on the manual counting of otolith annuli are labour-intensive, time-consuming, and subject to significant inter-reader variability. This study introduces SwinGPC-AgeRecognitioNet, a hybrid deep learning framework designed for efficient automated age estimation in the Mediterranean horse mackerel (Carangidae: Trachurus mediterraneus), to address these challenges. The proposed architecture synergises Swin Transformer-based feature extraction with a Gaussian Process Classifier (GPC) to capture global structural patterns while providing robust probabilistic predictions. The methodological workflow integrates three critical stages: (1) high-level feature extraction via Swin Transformer; (2) discriminative feature selection using Recursive Feature Elimination; and (3) hyperparameter-optimised classification via GPC. Experimental evaluations on a dataset of 1231 otolith images reveal that the proposed model consistently outperforms Convolutional Neural Networks architectures (e.g., VGG, ResNet), achieving accuracies of 88.66 % in multi-class classification and up to 94.33 % in binary tasks. These findings highlight the potential of SwinGPC-AgeRecognitioNet as a scalable, high-precision tool for fisheries science, offering a reliable alternative for data-driven resource management.
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    FishAgePredictioNet: A multi-stage fish age prediction framework based on segmentation, deep convolution network, and Gaussian process regression with otolith images
    (Elsevier, 2024) Isguzar, Seda; Turkoglu, Muammer; Atessahin, Tuncay; Durrani, Omerhan
    Fish ageing is a vital component of fisheries management as it enables the evaluation of fish population status and supports the development of sustainable management strategies. However, traditional methods of age determination through otolith analysis by experts are resource-intensive and time-consuming. Therefore, there is a growing demand for more cost-effective and automated techniques to accurately determine fish age. In accordance with this purpose, we proposed a multistage framework for fish age prediction using otolith images. First, otoliths in the images were detected using the Faster Region-based Convolutional Neural Networks (RCNN) model, which includes 8 convolution layers, and the detected otoliths were clipped. Subsequently, using a pretrained neural network based on the transfer learning approach, deep features were extracted separately from the right and left otolith images, and all the obtained features were combined. Finally, these combined features are given as input to the Gaussian process regression model. To evaluate the performance of the proposed architecture, 4109 images of right and left otoliths belonging to Greenland halibut (flatfish, Reinhardtius hippoglossoides) were used. The proposed architecture produced 1.83 MSE, 0.98 R-squared, 1.35 RMSE, and 10.29 MAPE scores in the experimental studies. As a result, the proposed model achieved superior performance compared with previous studies. Our findings show that our FishAgePredictioNet system could help experts predict fish age based on otolith images.
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    MobileNet- and attention-based MLP-Mixer architecture for geographical-region recognition of a marine fish (Perciformes: Carangidae) using otolith images
    (Pergamon-Elsevier Science Ltd, 2025) Durrani, Omerhan; Isguzar, Seda; Imak, Andac; Atessahin, Tuncay; Comert, Zafer; Durrani, Syeda Zahra; Turkoglu, Muammer
    Fishery management is crucial to sustain marine ecosystems by preventing overfishing and ensuring a fair distribution of fishing quotas. Accurately identifying the geographical origins of fish stocks is a key challenge in region-specific management strategies. Otoliths, calcified structures found in the heads of all fish species (except sharks and rays), provide insights into the life history and geographical origins of these fish. Traditional otolith analysis is time-consuming and error-prone because of manual inspection. Our study presents a novel approach using deep learning and computer vision to automate the geographical recognition of fish using otolith images. We propose a model that integrates MobileNet, which is known for its efficiency, with an advanced Mlp-Mixer that incorporates an attention mechanism to extract enhanced features. When tested on a diverse dataset of otolith images from five regions, the proposed model achieved a remarkable 96% accuracy, significantly outperforming traditional methods. This high accuracy demonstrates the potential to revolutionise fishery management by providing a fast, reliable, and automated solution for geographical region identification. In conclusion, the proposed method demonstrates the transformative potential of combining MobileNet and an attention-based Mlp-Mixer for automated fish geographic recognition using otolith images. This innovative method addresses the limitations of traditional manual inspection and paves the way for more effective and sustainable fishery management practices.