## 4 Experiments and Results
### Cornea Segmentation Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **Micro IoU** & **MAPE (Cornea Size)** \\ \hline SCED1 & 96.32 & 88.29 & \(8.02\pm 13.22\) \\ SCED2 & 96.74 & 89.50 & \(7.28\pm 11.25\) \\ SCCD3 & 96.97 & 90.21 & \(7.45\pm 12.48\) \\ SCED4 & 96.39 & 88.50 & \(8.14\pm 13.07\) \\ SCED5 & 96.62 & 89.18 & \(7.55\pm 11.19\) \\ \hline SCED1-HT & 96.40 & 88.37 & \(9.11\pm 14.11\) \\ SCED2-HT & 96.36 & 88.18 & \(9.06\pm 13.54\) \\ SCED3-HT & 96.49 & 88.56 & \(8.58\pm 13.28\) \\ SCED4-HT & 96.39 & 88.31 & \(9.09\pm 15.44\) \\ SCED5-HT & 96.48 & 88.56 & \(8.90\pm 12.67\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation results for the individual cornea segmentation models. Figure 9: The percentage for the highest IoU value of the top five candidate circles calculated from the objective function (Equation (1)). Finally, we combined all segmentation results from all SCED networks using a voting ensemble. The results from the ensemble models are shown in Table 3. The EM-5-SCED is an ensemble model of SCED1 to SCED5, while the EM-5-SCED-HT is an ensemble model of SCED1-HT to SCED5-HT. The results from the ensemble models outperform their individual models. The EM-5-SCED has the best performance for cornea segmentation in this experiment, achieving 97.08% accuracy, 90.58% IoU in micro-average, and 6.93% MAPE for cornea size. For the EM-5-SCED-HT results, the accuracy and IoU are comparable to EM-5-SCED but cannot yield a lower MAPE value, as EM-5-SCED-HT employs the HCT to identify the cornea area based on the segmentation results from EM-5-SCED. When EM-5-SCED generates inaccurate cornea shapes but still closely approximates cornea sizes, employing the HCT to determine the cornea region can result in incorrect areas, leading to errors in cornea size. To evaluate the efficacy of our proposed method, we conducted a comprehensive comparative analysis using both image processing and deep learning approaches for cornea segmentation. The first comparison focused on the image processing method developed by [PERSON] et al. [11], who proposed techniques for cornea and corneal ulcer segmentation. We implemented the process as described in their study to extract the cornea area for direct comparison with our method. The image processing method utilizes the Otsu thresholding algorithm [43] and morphological operators to determine the eye border pixels. Then, the upper and lower eye border pixels are fitted with Gielis curves [44]. Finally, the cornea region is obtained from the enclosed circle formed by the upper and lower eye border curves. The second approach involved the deep learning-based SLIT-Net model developed by [PERSON] et al. [45]. This model, based on the Mask R-CNN architecture [46], is designed for ocular staining image segmentation, including the limbus (cornea region). In the experiment, we utilized the SLIT-Net model originally provided by the authors, which was trained with 133 slit-lamp images from the University of Michigan Kellogg Eye Center (Ann Arbor, MI, USA) and Aravind Eye Care System (Madurai, India) without additional training on the SUSTech-SYSU dataset to generate cornea segmentation results for evaluation. Both methods were tested using the same dataset employed in our study to ensure a consistent comparison. Additionally, we included the U-Net and U-Net  
Figure 10: Sample results from the cornea segmentation algorithm. ResNet-50 models in the analysis to establish a comprehensive baseline for performance comparison. The results reveal that the EM-5-SCED and EM-5-SCED-HT variants of our proposed method both achieve superior cornea segmentation performance compared to all other examined methods. This enhanced performance is evident across multiple metrics, including Accuracy, IoU, and MAPE in cornea size estimation, as presented in Table 3.  
Figure 11 illustrates the cornea segmentation results of the eight methods: Image Processing Method [11], SLIT-Net [45], U-Net, U-Net-ResNet-50, SCED3, SCED3-HT, EM-5-SCED, and EM-5-SCED-HT.