## 4 Experiments and Results
### Corneal Ulcer Segmentation Results  
In the second part of the experiment, we built corneal ulcer segmentation models based on the original U-Net architecture [32]. The four model configurations used in the experiment are shown in Table 5. The input image size was \(512\times 512\times 3\), the kernel sizes of the convolutional layers varied between 3 and 5, and ReLU was used as the activation function of the convolutional layers. The depth of the encoder and decoder networks in all models was equal to four layers. For the encoder networks, the number of feature maps in the first layer was set to 32 or 64 and then doubled in the next set of convolutional layers. A max pooling layer with a stride of \(2\times 2\) was applied to the encoders of every model. In the decoder networks, the number of feature maps in each convolutional layer was the reverse sequence of the number of feature maps in the encoder networks. The upsampling process utilized a Conv2 DTranspose layer with a stride of \(2\times 2\). This setting was applied to the decoders of every model. A concatenation layer was included in the decoder networks to  
Figure 12: Sample of various circle detection methods for cornea segmentation. (Green = True Positive, Blue = False Negative, Red = False Positive)combine the skip-connected feature maps from the encoder networks. The final layer was characterized by one feature map and a sigmoid activation function. We compared three methods for corneal ulcer segmentation. The first method, named UNET-R-I, uses a conventional U-Net model with raw cornea mask multiplication in the inference phase. The model for segmenting the corneal ulcer area is based on the U-Net configuration in Table 5. The model outputs are multiplied with the cornea segmentation mask from EM-5-SCED. This step is applied to filter out the predicted corneal ulcer area that falls outside the boundary of the predicted cornea area. The remaining corneal ulcer region is the result of this process. This method is used as a baseline for comparison to our proposed method. The second method is our proposed U-Net model with HCT cornea mask multiplication in the learning phase, named UNET-H-L. This model uses the same U-Net configuration in Table 5 as the baseline model. The key differences between our proposed method and the baseline approach are: (1) instead of applying a raw cornea mask, we apply the circular cornea mask obtained from EM-5-SCED-HT, and (2) instead of applying a mask in the inference phase, the cornea mask is applied in both the learning and inference phases. The last method, named UNET-R-L, utilizes the same models as the proposed method in Figure 4, but replaces the circular cornea mask input from EM-5-SCED-HT with the raw predicted cornea mask from EM-5-SCED. The raw predicted cornea mask is considered a soft restriction in the learning phase, as it contains continuous values between 0 and 1, whereas the circular cornea mask is considered a hard restriction in the learning phase due to its binary values of 0 or 1. This method aims to observe the effect of using different masks during the learning phase. Figure 13 presents graphs showing the training and validation accuracy and loss curves for the corneal ulcer segmentation models in the experiment. These graphs illustrate the accuracy and loss values for both training and validation processes across all training epochs. The data represent the first fold of a three-fold cross-validation process for six sample models: UNET2-R-I, UNET4-R-I, UNET2-R-L, UNET4-R-L, UNET2-H-L, and UNET4-H-L. The graphs reveal that the UNET2-R-L, UNET4-R-L, UNET2-H-L, and UNET4-H-L models, all of which incorporate the cornea mask in the learning phase, converge faster than the conventional UNET2-R-I and UNET4-R-I models. This improved convergence occurs because these models focus on learning from the fluorescein-stained areas within the cornea region.