## 5 Conclusions and Discussion  
This paper proposes an automated corneal ulcer grading system using ocular staining images based on deep learning and the Hough Circle Transform. The algorithm consists of two main components, namely, the cornea segmentation and corneal ulcer segmentation modules, which together aim to assess the severity of corneal ulcers. The proposed method  
Figure 18: Sample of the severity grading results. Case (**a**,**d**,**f**) represent cases of incorrect severity grading for corneal ulcer. Case (**b**,**c**,**e**,**g**) represent cases of correct severity grading for corneal ulcer. utilizes a combination of deep learning techniques with cornea masks generated by the Hough Circle Transform applied in the learning phase. In our experiments, we utilized the SUSTech-SYSU dataset for training and testing both the cornea segmentation and corneal ulcer segmentation modules. The best performing method identified in this study achieved an accuracy of 86.15% in determining the severity degree of corneal ulcers. For cornea segmentation, the proposed method employs deep learning to segment corneal areas and utilizes the Hough Circle Transform to represent model outputs in circular shapes. We compared the outputs of this approach with raw results from deep learning models. The comparison shows that the proposed method does not provide better outcomes; however, applying the results from the Hough Circle Transform as masks in the learning phase while training the corneal ulcer segmentation models can improve model performance compared to the baseline method. Additionally, employing a voting ensemble to combine corresponding predicted images from each method significantly enhances the performance of cornea segmentation and corneal ulcer segmentation over individual models. In our comparison of corneal ulcer segmentation methods, the proposed method demonstrated superior performance, achieving improvements of 0.05% in Accuracy, 1.04% in Dice Similarity Coefficient, 3.07% in Sensitivity, 1.72% in Intersection over Union, and 0.19% in Mean Absolute Error compared to the baseline method relying on a deep learning technique with raw predicted cornea masks during the inference phase. Moreover, the implementation time was reduced by 13.88%  
However, this work has several limitations. The cornea shape in some images is represented by the elliptical; therefore, using Hough Circle Transform to locate the cornea area can cause inaccurate cornea area location and adversely affect corneal ulcer segmentation performance. Furthermore, in this study we were not able to accurately segment point-like and mixed point-flaky corneal ulcers due to a lack of labeled data for these cases in the dataset. Therefore, future work should focus on applying ellipse detection to locate corneal areas and preparing labeled data for point-like and mixed point-flaky corneal ulcers in order to further improve segmentation and severity grading performance. Conceptualization, K.P. ; methodology, K.P. ; software, D.M. ; validation, D.M. ; formal analysis, D.M. ; investigation, D.M. ; resources, D.M. and K.P. ; data curation, D.M. ; writing--original draft preparation, D.M. ; writing--review and editing, K.P. ; visualization, D.M. ; supervision, K.P. ; project administration, D.M. All authors have read and agreed to the published version of the manuscript. This research was partially supported by Chiang Mai University. Not applicable. The original contributions presented in the study are included in the article.