## 5 Analysis of Training Results
### Comparison of Results of Various Models on the Test Set  
To evaluate the performance of SE-TransUNet, comparative experiments were conducted against several commonly used semantic segmentation models, including Tran  
Figure 8: Box plot of model IOU, Dice, Recall, and Precision metrics. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experiment Round** & **IoU** & **Dice** & **Recall** & **Precision** & **AUC** & **MCC** & **F1-Score** & **FPS** \\ \hline
1 & 0.8425 & 0.8425 & 0.9641 & 0.8674 & 0.9828 & 0.8728 & 0.9382 & 6.45 \\
2 & 0.8542 & 0.8483 & 0.9626 & 0.8701 & 0.9840 & 0.8833 & 0.9493 & 6.50 \\
3 & 0.8025 & 0.8114 & 0.9203 & 0.8456 & 0.9753 & 0.8381 & 0.9035 & 6.56 \\
4 & 0.8462 & 0.8293 & 0.9288 & 0.8308 & 0.9867 & 0.8544 & 0.9166 & 6.59 \\
5 & 0.8139 & 0.8106 & 0.9212 & 0.8263 & 0.9375 & 0.8326 & 0.9013 & 6.57 \\ Mean & 0.8318 & 0.8304 & 0.9394 & 0.8480 & 0.9733 & 0.8562 & 0.9218 & 6.53 \\ Standard Deviation & 0.0224 & 0.0204 & 0.0221 & 0.0202 & 0.0204 & 0.0218 & 0.0213 & 0.057 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical table of various metrics for the validation set in 5 rounds of experiments. sUNet [22], UNet [23], DeeplabV3 plus [24], SegNet [25], BiSeNetV2 [26], FPN [27], DoubleUNet [28], NestedUNet [29], and Swin-UNet [30]. All models were trained and tested under the same hyperparameter settings and loss functions, with the model architecture being the only variable. The average values of various evaluation metrics on the test set for each model are summarized in Table 6. To assess the effect of incorporating the SE module into the original TransUNet architecture, a paired t-test was conducted between SE-TransUNet and TransUNet. The statistical results are presented in Table 7, with the degrees of freedom (df) of the paired t-test equal to 4. A significance level of \(\alpha=0.05\) was used, and \(p\)-values below this threshold were considered statistically significant. As shown in Table 6, notable performance differences are observed among the models. SE-TransUNet achieves the highest IoU and Recall, indicating improved overall coverage and reduced omission in leakage detection compared to other models such as UNet and DeepLabV3 plus. UNet reports the highest Dice score (0.8631), reflecting its strength in boundary-level similarity segmentation. FPN outperforms others in terms of Precision and AUC, suggesting more stable probabilistic predictions. In terms of inference efficiency and computational complexity, there is a clear inverse relationship between model parameter size and FPS. BiSeNetV2 has the fewest parameters and the highest FPS, but its segmentation accuracy is relatively lower. SE-TransUNet, with the highest parameter count and lowest FPS, exhibits slightly better accuracy metrics. Table 7 further indicates that SE-TransUNet shows a statistically significant improvement over TransUNet in IoU and Recall, which reflects more complete leakage detection and better control of missed areas, albeit at the cost of reduced inference speed. No significant differences were found in other metrics such as Dice. In practical applications, model selection should be guided by the trade-off between detection accuracy and computational efficiency, depending on the specific requirements of the deployment scenario.