Classification and comparison of glaucoma detection methods in retinal fundus images using SVM and U-Net2

In medical image processing, early detection of eye diseases like glaucoma is crucial to prevent blindness. This study evaluates two deep learning models—Support Vector Machine and U-Net—for classifying retinal fundus images to improve glaucoma detection. Using 316 images from "The Brazil Glaucoma Database," the study applied various preprocessing techniques such as resizing, grayscale conversion, and edge enhancement. Optical disc and cup segmentation was achieved with Hough transform and circular masking. The SVM model outperformed U-Net, achieving 95% accuracy compared to U-Net's 88%. SVM showed better precision, recall, and F1-scores, making it more reliable for distinguishing between normal and glaucoma images. While U-Net had strong recall for glaucoma detection, its lower precision and accuracy indicate room for improvement. Future work should focus on refining U-Net to enhance its precision and overall performance.