Simplifying Glaucoma Diagnosis with U-Net on Retinal Images

Visual impairment caused by glaucoma is a commonly observed phenomenon around the world.As it progressively damages the optic nerve fibers, it is incurable in its later stages.Therefore, early detection serves an essential purpose in the aging society to prevent irreversible vision loss.One of the diagnostic factors for glaucoma is the evaluation of the Cup-to-Disc Diameter ratio.To detect glaucoma, an existing pipeline is used, initially Data Preprocessing is implemented to eliminate all the noise from images and then the partition of the optic disc and cup is executed, continued using the evaluation of ratio values among cup and disc, which is then used to make a prediction.To fragment the optic disc, a threshold-based steps are employed.However, segmenting the optic cup is a challenging problem that has been tackled by numerous algorithms.The suggested techniques involve this problem by introducing an innovative approach for segmenting the optic cup.A modified area enhanced steps is implemented to partition the optic cup area, which is continued by morphological operations and infilling blood vessels.Through the partitioned images, the ratio values of the optic cup and disc are determined, and the values are fed to an SVM pattern for classification.The metrics demonstrate that the suggested technique can rigorously speculate the appearance of glaucoma with minimal computational complexity.In general, the suggested approach is a successful and efficient way to divide the optic cup for glaucoma analysis.