Classification of Freshwater Fish Diseases in Bangladesh Using a Novel Ensemble Deep Learning Model: Enhancing Accuracy and Interpretability

Effective disease management and mitigation strategies for fish diseases depend on timely and accurate diagnosis. In recent years, artificial intelligence methods-classification algorithms in particular-have become effective instruments for automating fish disease diagnosis. This paper presents two types of ensemble models: i) the baseline averaged ensemble (AE) model and ii) the novel Performance Metric-Infused Weighted Ensemble (PMIWE) model. By leveraging pre-trained models and novel ensemble techniques, we achieve a testing accuracy of 97.53%, corresponding precision, recall, and F1-score of 97%. We also bring about enhanced interpretability and trustworthiness using the Grad-CAM (Gradient-weighted Class Activation Mapping) explainable artificial intelligence (XAI) technique.