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

Abstract

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.

Keywords

• Deep learning
• transfer learning
• ensemble model
• fish diseases
• aquaculture
• Grad-CAM