# A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet
## 3 Experiments and Discussion
### Accuracy across Different Categories  
To delve deeper into the limitations of the original EfficientNet model in the classification task of fish feeding behavior images, this study compared the confusion matrices of the unoptimized EfficientNet model and the MIPS-enhanced MIPS-EfficientNet model. Figure 8 displays the classification efficacy of the original EfficientNet model, which accurately recognizes most categories. However, it exhibits a higher rate of misclassification, particularly in the category corresponding to subtle feeding behavior, likely due to the model's over-sensitivity to background features in the images. Moreover, as revealed in Figure 8, the MIPS-EfficientNet model, after the introduction of MIPS, shows improvements primarily in the recognition of the fourth category. The performance for the other categories remains stable or shows only minor changes. This indicates that the MIPS enhancement strategy effectively optimizes the model's accuracy in classifying fish feeding behavior, especially for subtle behavioral distinctions. The MIPS approach, by optimizing the model's feature extraction and representation capabilities, diminishes the interference of background noise on classification decisions. Consequently, it enhances the model's ability to recognize fish behavior against complex backgrounds while improving classification accuracy. These findings affirm the importance and efficacy \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Test-Acc (\%)** & **Recall (\%)** & **Precision (\%)** & **F1 Score (\%)** & **Training Time (Hours)** \\ \hline Efficientnet & 88.81\% & 82.35\% & 87.27\% & 80.68\% & 1.595 h \\ MIPS-Efficientnet & 97.00\% & 93.56\% & 94.10\% & 93.78\% & 1.595 h \\ Resnet-18 & 96.27\% & 94.35\% & 94.92\% & 94.44\% & 2.150 h \\ MIPS-Resnet-18 & 98.50\% & 97.54\% & 97.00\% & 97.13\% & 2.150 h \\ Resnet-50 & 95.50\% & 85.29\% & 88.79\% & 86.27\% & 2.365 h \\ MIPS-Resnet-50 & 97.80\% & 93.37\% & 94.96\% & 94.03\% & 2.365 h \\ ResNeXt50 & 93.28\% & 90.70\% & 91.38\% & 90.46\% & 2.291 h \\ MIPS-ResNeXt50 & 94.78\% & 92.28\% & 92.14\% & 92.19\% & 2.291 h \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with other CNNs. of the MIPS module in enhancing the performance of image classification tasks in complex underwater environments. The MIPS-EfficientNet model demonstrated a significant improvement in accuracy, reaching 97.00%, which is substantially higher than the EfficientNet model's 88.81%. Accuracy represents the overall proportion of correct predictions, indicating that MIPS-EfficientNet is more precise in its overall predictions. MIPS-EfficientNet also showed improvement in recall, achieving 93.56%, compared to EfficientNet's recall of 82.35%. Recall measures the model's ability to correctly identify positive instances; thus, MIPS-EfficientNet performs better in correctly identifying instances of each category. In terms of precision, MIPS-EfficientNet also surpassed EfficientNet, with scores of 94.10% and 87.27%, respectively. Precision refers to the proportion of instances predicted as positive that are actually positive, with higher precision indicating fewer false positives (misreports). Lastly, MIPS-EfficientNet significantly improved its F1 score, reaching 93.78%, compared to EfficientNet's F1 score of 80.68%. The F1 score is the harmonic mean of precision and recall, indicating that MIPS-EfficientNet is more balanced and superior in overall performance. In summary, the MIPS-EfficientNet model shows significant improvements across all primary performance metrics. These metrics suggest that compared to the original EfficientNet model, MIPS-EfficientNet has made progress in reducing misjudgments (improving precision), enhancing the identification rate of positive instances (improving recall), and achieving a better balance between the two (improving the F1 score). These improvements may be attributed to optimizations in the model structure, training process, or data handling by MIPS-EfficientNet.