## 4 Experiments and Results
### Severity Grading Results  
To grade the severity of corneal ulcers, the corneal area is divided into five parts based on the TG grading criteria. The central zone of the cornea is represented by a circle or an  
Figure 16: Case (**a**) represents a case of predicting a higher percentage of ulcerated area than the actual value displayed in Figure 15. Case (**b**,**c**) represent cases of predicting a lower percentage of ulcerated area than the actual value displayed in Figure 15. (Green = True Positive, Blue = False Negative, Red = False Positive).  
Figure 15: Scatter plot showing the predicted and actual percentage of ulcerated area on the cornea from EM-4-UNET-H-L.  
ellipse with a radius equal to one-third of the cornea's radius. The outer area around the central zone is divided equally into four quadrants, as shown in Figure 5. To evaluate the performance of severity grading, we used 390 staining images, each graded twice. The first grading result, called the actual grade, was obtained using the actual corneal area and actual corneal ulcer area as inputs. The second grading result, called the predicted grade, was obtained using the predicted corneal area from the EM-5-SCED-HT model and predicted corneal ulcer area from the EM-4-UNET-H-L model as inputs. Among the 390 test images, there were 36 images of grade0 cases, 88 images of grade1 cases, 28 images of grade2 cases, 6 images of grade3 cases, and 232 images of grade4 cases. The proposed method achieves an accuracy of 86.15% in grading the severity of corneal ulcers; the confusion matrix is illustrated in Figure 17. The system was able to correctly grade 77.78% (28 out of 36 images) of grade0 cases, 71.59% (63 out of 88 images) of grade1 cases, 60.71% (17 out of 28 images) of grade2 cases, 83.33% (5 out of 6 images) of grade3 cases, and 96.12% (223 out of 232 images) of grade4 cases.  
Errors in severity grading occur due to inaccurate cornea segmentation and corneal ulcer segmentation. Most errors involve predictions that are off by one grade level. For instance, 11.11% of Grade0 classes were mistakenly predicted as Grade1, 11.36% of Grade1 classes were incorrectly classified as Grade2, and 35.71% of Grade2 classes were misclassified as either Grade1 or Grade3. Even minor segmentation errors for both cornea and corneal ulcers can lead to significant grading inaccuracies. For example, in Figure 18 case (a), the system incorrectly identified a small area in the cornea as an ulcer where none existed, leading to an erroneous severity grade of Grade1 for an image that actually contained no corneal ulcer. In case (d), an incorrect segmentation of the corneal ulcer area at the top-left of the cornea resulted in the ulcer areas covering three quadrants surrounding the central area instead of two. Moreover, when the ulcer area is close to the boundary of each section, the chance of grading errors increases significantly. For example, in case (f), there was a slight error in predicting the ulcer area at the edge of the pupil region. The system failed to detect an ulcer in the pupil area, and the segmented cornea area was slightly smaller than the actual one. These errors led to an assigned severity grade of Grade2, whereas the correct grade was Grade4. Furthermore, 10.22% of Grade1 classes were predicted as  
Figure 17: Confusion matrix for corneal ulcer grading.  
Grade4 classes. This occurred due to erroneous predictions of the ulcer area on the cornea which extended into the central section of the corneal area, causing the system to grade the severity as higher than it actually was. Notably, the dataset used in this experiment has a limitation, as it contains an insufficient number of Grade2 and Grade3 corneal ulcer cases, which are crucial for accurately evaluating the algorithm's performance across all severity degrees of the condition.