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Age Related Macular Degeneration (ARMD) is an eye sickness prompts obscured territory close to the focal point of vision or it might create clear spots in the focal vision, and articles may not show up as splendid as they used to be and loss of vision. The WHO revealed that 8 million matured individuals are influenced with ARMD. The infection is essentially grouped into two sorts, wet and dry types of ARMD. The presence of Drusen and Choroidal Neovasculation(CNVs) are the manifestations of dry and wet type of ARMD. The drusen is the yellow-white store related with diminishing or hypo-pigmentation of the retinal pigment epithelium. CNVs lead to draining and scarring causing loss of vision. The diagnosis of ARMD is performed naturally by dissecting the retinal images. In the proposed work, the milestone of retina Optic disc is recognized and veiled by roundabout hough change and morphological methodology. The Drusen is distinguished by nearby and versatile seeded region growing. The sequence of Mathematical morphology operation is utilized to fragment CNVs in STARE database. The performance measures, for example, SE, SPE, and ACC esteems acquired are {98%, 99%, 99%}, {62.86%, 99.39%, 96.95%} for division of drusen and CNVs separately. The deep learning based classification network is used for Classification of Age related Macular degeneration (CO-ARMD)

Diabetes is characterized by constant high level of blood glucose. The human body needs to maintain insulin at very constrict range. The patients who are all affected by diabetes for a long time affected by eye disease called Diabetic Retinopathy (DR). The retinal landmarks namely Optic disc is predicted and masked to decrease the false positive in the exudates detection. The abnormalities like Exudates, Microaneurysms and Hemorrhages are segmented to classify the various stages of DR. The proposed approach is employed to separate the landmarks of retina and lesions of retina for the classification of stages of DR. The segmentation algorithms like Gabor double-sided hysteresis thresholding, maximum intensity variation, inverse surface adaptive thresholding, multi-agent approach and toboggan segmentation are used to detect and segment BVs, ODs, EXs, MAs and HAs. The feature vector formation and machine learning algorithm used to classify the various stages of DR are evaluated using images available in various retinal databases, and their performance measures are presented in this paper.