Hybrid Architectures Combining Cellular and Convolutional Neural Networks for Fish Classification and Disease Detection

Quang Hong Doan; Hoan Quang Nguyen; Truyen The Nguyen; Anh Duc Duong

doi:10.21553/rev-jec.402

Hybrid Architectures Combining Cellular and Convolutional Neural Networks for Fish Classification and Disease Detection

Quang Hong Doan, Hoan Quang Nguyen, Truyen The Nguyen, Anh Duc Duong

Abstract

Fish classification and disease detection are crucial for (play crucial role in) sustainable aquaculture management, requiring high-accuracy, real-time computer vision models. This study introduces FISH-YOLOv8, an enhanced deep learning model built on YOLOv8, replacing all convolutional layers with Cellular Neural Networks (CeCNNs) to leverage their superior dynamics and noise tolerance for improved feature extraction in turbid, occluded underwater conditions. BiFormer Attention and Non-Maximum Suppression (NMS) further optimize detection accuracy and speed (enhance detection accuracy and processing speed). Evaluated on a Roboflow dataset of 1,800 images across 14 classes (10 fish species, 4 diseases), FISHYOLOv8 achieves a Mean Average Precision (mAP) mAP@50 of 0.9936 ± 0.0012 (p < 0.05) and 98.89% accuracy after 50 epochs, outperforming YOLOv8 and peers. With 52 ± 2 Frames Per Second (FPS), it offers a robust, real-time solution for aquaculture monitoring.

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DOI: http://dx.doi.org/10.21553/rev-jec.402

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