Enhanced Fish Health Monitoring in Aquaculture with Attention-Based Deep Learning Technique

Fish is a key protein source, with rising global demand placing pressure on aquaculture to maintain fish health and productivity. However, diseases like Bacterial Gill Disease, Aeromoniasis, and Parasitic Disease pose serious threats to the industry, and early detection is crucial to prevent outbreaks. This study investigates the application of deep learning models, par-ticularly EfficientNet and CBAM (Convolutional Block Attention Module), to classify fish diseases with improved accuracy. Our proposed models were tested on a multi-class fish disease dataset, where EfficientN etB6+CBAM achieved the highest classification accuracy of 99.45% and a superior F1-score across disease types. The results emphasize the significance of attention mechanisms in enhancing feature extraction from complex datasets, providing a highly accurate and accessible disease detection tool for fish farmers. This approach marks a promising advancement in sustainable aquaculture by facilitating intelligent disease man-agement through deep learning technologies.