## 4 Experiments and Results
### Corneal Ulcer Segmentation Results
UNET2-H-L, which applies a kernel size of 5 and the same feature map size. As a result, UNET4-H-L stands out as the best method in this phase. Next, we employed a voting ensemble to combine the corneal ulcer segmentation results from four models within each method and evaluate their performance. Accordingly, EM-4-UNET-R-I was an ensemble of models from UNET1-R-I to UNET4-R-I, EM-4-UNET-R-L was an ensemble of models from UNET1-R-L to UNET4-R-L, and EM-4-UNET-H-L was an ensemble of models from UNET1-H-L to UNET4-H-L. The results of the ensemble approach in Table 7 demonstrate that the proposed method (EM-4-UNET-H-L) outperforms the conventional method (EM-4-UNET-R-I) in terms of all metrics. Accuracy increases by 0.05%, DSC increases by 1.04%, Sen increases by 3.07%, IoU increases by 1.72%, and MAE decreases by 0.19%. Moreover, using the segmented cornea area from HCT multiplication in the learning phase (EM-4-UNET-H-L) yields better corneal ulcer segmentation result than using the raw predicted cornea mask (EM-4-UNET-R-L) for all metrics. Notably, Accuracy increases by 0.07%, DSC increases by 1.14%, Sen increases by 2.22%, IoU increases by 1.88%, and MAE decreases by 0.13%. The voting ensemble technique enhances the segmentation performance of the UNET-H-L method, which can be observed by comparing the results of UNET4-H-L, with the best performance in the UNET-H-L group, to EM-4-UNET-H-L. The accuracy increases from 99.35% to 99.37%, the DSC increases from 89.87% to 90.00%, and the IoU increases from 81.60% to 81.83%. However, Sen decreases from 89.17% to 88.19%, and the MAE remains similar. Based on these results, the proposed method (EM-4-UNET-H-L) outperforms all other corneal ulcer segmentation methods in this experiment. Sample results from the highest-performing instance of each method are shown in Figure 14. This comparison illustrates that the proposed UNET4-H-L method segments the corneal ulcer region more accurately than UNET2-R-I and UNET2-R-L. This can be observed from the true positive areas in UNET4-H-L, which more comprehensively fill the corneal ulcer area in the eye images. Furthermore, the false negative and false positive areas from UNET-4-H-L are slightly smaller than the outcomes from UNET2-R-I and UNET-2-R-L. These consequences are also evident in the ensemble model results. Upon visual \begin{table}
\begin{tabular}{c 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 UNET1-R-I & 99.25 & 88.21 & 86.37 & 78.91 & 2.74 \(\pm\) 5.33 & 20.8 GB & 38 s/epoch \\ UNET2-R-I & 99.32 & 89.22 & 86.91 & 80.54 & 2.56 \(\pm\) 5.09 & 21.1 GB & 42 s/epoch \\ UNET3-R-I & 99.22 & 87.87 & 86.63 & 78.36 & 2.82 \(\pm\) 5.52 & 14.6 GB & 36 s/epoch \\ UNET4-R-I & 99.29 & 88.57 & 85.43 & 79.48 & 2.82 \(\pm\) 5.80 & 21.0 GB & 40 s/epoch \\ \hline UNET1-R-L & 99.20 & 87.54 & 86.90 & 77.85 & 2.87 \(\pm\) 5.90 & 20.8 GB & 44 s/epoch \\ UNET2-R-L & 99.27 & 88.64 & 88.53 & 79.60 & 2.60 \(\pm\) 5.11 & 21.1 GB & 49 s/epoch \\ UNET3-R-L & 99.21 & 87.55 & 85.73 & 77.85 & 2.80 \(\pm\) 5.26 & 14.6 GB & 42 s/epoch \\ UNET4-R-L & 99.23 & 87.86 & 86.37 & 78.34 & 2.69 \(\pm\) 4.98 & 21.0 GB & 47 s/epoch \\ \hline UNET1-H-L & 99.29 & 88.87 & 88.31 & 79.96 & 2.74 \(\pm\) 5.37 & 20.8 GB & 44 s/epoch \\ UNET2-H-L & 99.32 & 89.51 & 90.15 & 81.02 & 2.65 \(\pm\) 5.31 & 21.1 GB & 50 s/epoch \\ UNET3-H-L & 99.26 & 88.38 & 87.57 & 79.17 & 2.77 \(\pm\) 5.38 & 14.6 GB & 42 s/epoch \\ UNET4-H-L & 99.35 & 89.87 & 89.17 & 81.60 & 2.51 \(\pm\) 4.68 & 21.0 GB & 49 s/epoch \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of all individual corneal ulcer segmentation models.