An Enhanced Pre-Processing Technique For Eye Image Segmentation

Early diagnosis is essential to controlling glaucoma, a progressive disorder that results in irreversible vision loss. Retinal fundus imaging has emerged as a crucial diagnostic technique for retinal disorders, such as glaucoma, in recent times. But because of the intricate vascular patterns and intricate architecture of the retina, it can be difficult to analyze these images appropriately. Areas of interest in fundus pictures are frequently found using image segmentation algorithms; however, this can be challenging. This study examines a variety of segmentation techniques, emphasizing both conventional and cutting-edge ways for splitting retinal fundus pictures. It also covers assessment metrics that are used to evaluate how well segmentation algorithms perform in identifying minute structural alterations associated with glaucoma. The study also looks at how deep learning algorithms can be implemented to separate optic disc and cup. Deep learning models typically perform worse with larger datasets, even though they have demonstrated good accuracy with smaller datasets. The investigation underscores the need for more sophisticated deep learning methodologies and efficient segmentation techniques, as it shows that effectively segmenting the optic disc (OD) and cup (OC) remains a considerable challenge, particularly for large and complex datasets.