## 4 Experiments and Results
### Cornea Segmentation Results  
For the first part of this experiment, we built SCED networks for segmenting the cornea area by using corneal fluorescein-stained images as inputs. Five SCED networks with different configurations were tested, as shown in Table 1. For the encoder network, the input size was set to \(512\times 512\times 3\). The feature map for the convolutional layer in the encoder network was set to 64, followed by the Rectified Linear Unit (ReLU) activation function and max pooling with a stride of \(2\times 2\). The kernel size of the convolutional layer varied from 7 to 11 and the depth of the CNN layers varied from 3 to 5. Dropout layers were included to prevent overfitting. For the decoder network, the upsampling size was \(2\times 2\), while the other parameters were defined the same as in the encoder network. The decoder's output layer consisted of one feature map with a sigmoid activation function. The model depth and other parameters were referenced from the work by [PERSON] et al. (2018). In this experiment, aiming to obtain a smooth shape of the predicted cornea area, we increased the kernel size to expand the receptive field. The differences among the models' architectures are as follows: SCED1-3 used a kernel size of 9 for all models and varying model depths of 3, 4, and 5, respectively; SCED4 increased the kernel size to 11 with a model depth of 3; and SCED5 employed different kernel sizes in each layer. In the encoder network, the kernel size for depth layers 1 and 2 were configured as 11, layers 3 and 4 were configured as 9, and layer 5 was configured as 7, maintaining this setup in the corresponding layers of the decoder network. The memory (VRAM) and training time per epoch required for training each model are also presented in Table 1.  
To benchmark cornea segmentation performance, we implemented two U-Net models. The first U-Net model followed the original design proposed by [PERSON] et al. (2016), while the second used transfer learning by combining the pretrained ResNet-50 encoder [PERSON] et al. (2016) with U-Net's decoder structure. The details of U-Net and U-Net-ResNet-50 are provided in Table 1.  
The training and validation accuracy and loss curves for the cornea segmentation models, illustrated in Figure 7, provide crucial insights into the models' learning process and performance. These graphs display the accuracy and loss metrics for both training and validation sets throughout the training epochs. The data presented in these graphs represent the first fold of a three-fold cross-validation process for all models. The plots reveal that all SCED models reach convergence within 30 to 40 epochs. Among the SCED model variants, the SCED3 model stands out by converging the fastest and achieving the highest accuracy on the validation set. Conversely, U-Net and U-Net-ResNet-50 show signs of overfitting, as evidenced by the discrepancy between training and validation performance. This can be observed in the accuracy and loss curves, where the training set accuracy continues to improve while the validation loss progressively increases from the early epochs. In this study, model selection is based on the criterion of minimal validation loss observed during the training process. As mentioned in Section 3, the raw outputs from SCED networks are postprocessed by image processing techniques and the HCT. In the experiment, we name the SCED networks with this postprocessing the Simple CNN Encoder-Decoder Model with Hough Circle Transform (SCED-HT). First, we utilized morphological erosion to separate excess pixels from the main cornea predicted area using a circular kernel with a radius of 4. Then, the connected component technique was applied to remove noise areas smaller than 400 pixels. After obtaining the desired area, the cornea region was expanded back to the original size using morphological dilation with the previous circular kernel. Subsequently, we applied HCT to locate the entire circle under the specified parameters.