## 4 Experiments and Results
### Corneal Ulcer Segmentation Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Accuracy** & **Micro DSC** & **Micro Sen** & **Micro IoU** & **MAE** & **Memory Usage** & **Training Time per Epoch** \\ \hline EM-4-UNET-R-I & 99.32 & 88.96 & 85.12 & 80.11 & 2.70 \(\pm\) 5.28 & 21.1 GB & 39 s/epoch \\ EM-4-UNET-R-L & 99.30 & 88.86 & 85.97 & 79.95 & 2.64 \(\pm\) 4.78 & 21.1 GB & 46 s/epoch \\ EM-4-UNET-H-L & 99.37 & 90.00 & 88.19 & 81.83 & 2.51 \(\pm\) 4.63 & 21.1 GB & 46 s/epoch \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the corneal ulcer segmentation ensemble models. examination, EM-4-UNET-H-L can segment the corneal ulcer area most accurately; these results are related to the experimental results in Tables 6 and 7. We summarize the results from our corneal ulcer segmentation approach in comparison to conventional methods in Tables 8 and 9. Each method is described in detail as follows:  
1. U-Net: This method uses the UNET2 model, which provided the best result of all U-Net models in this experiment, to segment the corneal ulcer area from the dataset. This method does not apply the cornea mask to filter out the segmented corneal ulcer area outside the cornea area. 2. U-Net-ResNet-50: We developed U-net-ResNet-50 by utilizing the encoder network from the ResNet-50 backbone combined with the decoder network from the U-Net architecture to segment the corneal ulcer area. This method also does not apply the cornea mask. 3. U-Net with raw cornea mask: The results of this method were derived from UNET2-R-I, which uses the corneal ulcer area from the UNET2 model and applies a raw cornea mask from EM-5-SCED during the inference phase to remove the excess corneal ulcer area outside the cornea area. Figure 14: Comparison of corneal ulcer segmentation results. (Green = True Positive, Blue = False Negative, Red = False Positive)  
4. Ensemble U-Net models with cornea mask: This method uses the ensemble segmented corneal ulcer areas from UNET1-R-I to UNET4-R-I (EM-4-UNET-R-I), which applies a raw cornea mask from EM-5-SCED in the inference phase. 5. Proposed method: The results of the proposed method are from the ensemble segmented corneal ulcer area from UNET1-H-L to UNET4-H-L (EM-4-UNET-H-L), which applies the cornea mask from EM-5-SCED-HT in the learning phase and inference phase. The comparison included the maximum memory usage evaluated during training and the total training time, representing the end-to-end process for training each method. For conventional methods, U-Net outperforms U-Net with the ResNet-50 backbone across all performance metrics. However, the U-Net model requires more resources in terms of training time and memory usage. Notably, the U-Net-ResNet-50 model underperforms compared to the other methods due to overfitting during model training. In comparing U-Net and U-Net with raw cornea masks, the results show that utilizing the predicted cornea mask to eliminate excess predicted corneal ulcer areas improves segmentation results across all metrics. However, this improvement comes at the cost of increased training time due to the additional step of training the cornea segmentation models. The proposed method demonstrates superior performance, outperforming all conventional methods across all metrics used in this experiment. Compared to the ensemble U-Net models with raw cornea masks, which have the same number of trainable parameters and consume the same maximum memory, the proposed method not only provides better performance but also requires less training time due to faster convergence. These results demonstrate that applying HCT cornea mask multiplication in the model learning phase can improve both performance outcomes and resource efficiency. In Figure 15, the graph represents a scatter plot between the actual and predicted percentage of ulcerated area on the cornea obtained from our best model (EM-4-UNET-H-L). From 354 images analyzed, 94.65% (335 images) fall within a margin of error of \(\pm\)10%, while 86.72% (307 images) fall within a margin of error of \(\pm\)5%.