## 4 Experiments and Results
### Corneal Ulcer Segmentation Results
Additionally, the validation set accuracy is significantly higher for these models due to the elimination of excess areas from the corneal ulcer segmentation through the use of the cornea mask. The selection criterion for the final model is the achievement of the least validation loss over the course of training. The performance of all methods is evaluated by comparing the predicted corneal ulcer area with the labeled images in terms of Accuracy, DSC, Sen, and IoU. Additionally, the MAE is used to compare the percentage of ulcerated area on cornea between the predicted and actual data. The values following MAE represent the standard deviation of the MAE, indicating the variability of the error around the mean in the predicted percentage of ulcerated area on the cornea. The results from this experiment are shown in Table 6. For segmentation performance, UNET2-R-I performs the best among all baseline models (UNET-R-I group), with an Accuracy of 99.32%, DSC of 89.22%, Sen of 86.91%, IoU of 80.54%, and MAE of 2.56%. The best model of the UNET-R-L group is UNET2-R-L, which segments corneal ulcers with an Accuracy of 99.27%, DSC of 88.64%, Sen of 88.53%, IoU \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Feature Map** & **Kernel Size** & **Depth** & **Trainable Parameters** \\ \hline UNET1 & 32, 64, 128, 256, 512 & 5 & 4 & 21,706,949 \\ UNET2 & 64, 128, 256, 512, 1024 & 5 & 4 & 86,811,013 \\ UNET3 & 32, 64, 128, 256, 512 & 3 & 4 & 9,154,245 \\ UNET4 & 64, 128, 256, 512, 1024 & 3 & 4 & 36,605,317 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Corneal ulcer segmentation model architecture. of 79.60%, and MAE of 2.60%. In the proposed method (UNET-H-L group), UNET4-H-L provides the best performance, achieving an Accuracy of 99.35%, DSC of 89.87%, Sen of 89.17%, IoU of 81.60%, and MAE of 2.51%. Comparing the results from the three main methods with similar U-Net architectures, UNET-H-L provides superior results in all metrics except for the MAE of UNET2-R-I and UNET2-R-L, which are slightly better than UNET2-H-L. The UNET-R-I and UNET-R-L methods demonstrate comparable performance in corneal ulcer segmentation tasks. This is because both methods use similarly shaped cornea masks to eliminate the fluorescein-stained areas outside the segmented cornea region. As a result, the outcomes do not differ significantly. In terms of memory (VRAM), the usage for each model depends on the trainable parameters, as shown in Tables 5 and 6. Models using the UNET2 configuration have the highest number of parameters and require the longest training time per epoch across all model groups. Regarding training time per epoch, the UNET-R-L and UNET-H-L groups are quite similar. Meanwhile, models in both of these groups take longer times to train per epoch compared to models in the UNET-R-I group with the same configuration. This is because an additional image (the cornea mask) needs to be loaded and processed in the model learning phase. In the context of varying model architectures, utilizing a kernel size of 5 generally yields better results than a kernel size of 3 across all metrics within the same model architecture for all methods except for UNET4-H-L, which uses a kernel size of 3. This method achieves better Accuracy, DSC, IoU, and MAE values than UNET2-H-L, which uses a kernel size of 5, by 0.03%, 0.36%, 0.58%, and 0.14%, respectively. For feature map size, models using larger feature map sizes tend to outperform models using smaller feature map sizes in all metrics except for the Sen of UNET4-R-I, which is lower than UNET3-R-I (refer to Tables 5 and 6). Moreover, utilizing a larger feature map with a kernel size of 5 results in UNET2-R-I and UNET2-R-L being the most efficient models in the UNET-R-I and UNET-R-L groups, respectively. However, the proposed UNET4-H-L method, which applies a kernel size of 3 and a large feature map size, provides better outcomes than  
Figure 13: Training and validation accuracy and loss curves for the corneal ulcer segmentation models.