## 2 Related Work  
Traditionally, the severity of corneal ulcers has been assessed by ophthalmologists through manual grading. For instance, the Oxford Grading System (OGS) is widely used as a standard for evaluating point-like corneal ulcers. Specialists need to compare the corneal ulcer area with the OGS index, which is divided into five grades, for manual assessment of symptoms [3; 7]. This method may yield imprecise diagnostic results, as patients with varying symptoms could be categorized within the same severity grade. In addition, to assess the severity of flaky corneal ulcers, ophthalmologists use specialized software such as Image-J [5] to locate or count the pixels in the corneal ulcer region for diagnosis. These manual processes are notably time-consuming and may lead to inconsistencies in subsequent diagnostic results. In response to these challenges, [PERSON] et al. [3] proposed an automated grading system for Dry Eye Syndrome (DES) based on the OGS using fluorescein-stained images. Their method involved using the HCT for corneal area segmentation and utilizing the green channel to identify damaged areas caused by DES. They applied a connected component algorithm to count dots in the corneal area and determined a correlation between the number of dots and DES severity using linear regression. However, this technique is only suitable for point-like corneal ulcers, not flaky corneal ulcers. There has been increasing interest in the development of automated segmentation algorithms for identifying flaky corneal ulcer areas. [PERSON] et al. [8] utilized the Gaussian Mixture Model (GMM) and Otsu thresholding techniques to segment corneal ulcers. They used 150 fluorescein-stained corneal ulcer images from Zhongshan Ophthalmic Center, Sun Yat-sen University. Their algorithm first converted these images to grayscale and applied gamma correction, Otsu binarization, and morphological operations to obtain the segmentation result. Additionally, they transformed the same images into a Hue Saturation Value (HSV) color space and utilized the GMM to segment the area of interest. Finally, they combined the results from both methods. In fluorescein-stained eye images, fluorescent staining outside the corneal area can lead to incorrect localization of the corneal ulcer area. To enhance the accuracy of identifying the corneal ulcer area, it is crucial to first establish a reliable delineation of the cornea area. In research on iris segmentation, [PERSON] et al. [9] identified the iris area in eye images using the U-Net model, emphasizing performance enhancement through adjustments to depth and batch normalization layers. On the other hand, [PERSON] et al. [10]implemented a corneal detection algorithm using edge detection and HCT to locate the cornea and pupil. Additionally, they employed the Linear Hough Transform to locate the eyelid boundary to specify the iris area. [PERSON] et al. [5] proposed a superpixel-based method for corneal ulcer segmentation using 150 fluorescein-stained images from Zhongshan Ophthalmic Center. Their methodology involved employing an iris locating algorithm from previous research and applying the coordinates to an ellipse equation. Corneal ulcer segmentation was achieved using a superpixel segmentation technique and a Support Vector Machine (SVM) classifier, followed by morphological operations to enhance system performance. [PERSON] et al. [11] applied image processing and eye-border recognition to locate the eye border in the image. They determined the iris region by fitting a circle into the eye border area. Finally, they located the corneal ulcer area by extracting the green channel from the image, which was masked by the iris region. Recently, deep learning techniques have been employed in eye disease for classification tasks [12; 13; 14] and segmentation tasks [4; 11; 15; 16]. [PERSON] et al. [15] manually selected the cornea area and trained a Deep Neural Network (DNN) model to segment the corneal ulcer area, selecting the result from the intersection area. [PERSON] et al.