Published Paper Details:

Human eyes can develop a variety of abnormalities as a result of age, injury, and illnesses including diabetes. The main causes of blindness in the globe are diabetic retinopathy, cataracts, macular degeneration, and glaucoma. Early detection and diagnosis of various eye conditions is essential for prompt treatment and stop eyesight loss. This approach can be aided by the diagnosis of many eye diseases through the analysis of medical photographs. Using deep learning, a number of eye disorders may be detected. These include acquiring images, extracting features, classifying or detecting diseases, and extracting regions of interest. In this paper, we suggested a model to identify eye illnesses as uveitis, glaucoma, crossed eyes, bulging eyes, and cataracts using deep learning methods, ResNet and VGG16. We used to get a 92% accuracy rate with ResNet50 and 79% accuracy with the VGG16 model . We can improve accuracy and detection rate while saving doctors' time by automating the detection procedure. The suggested methodology can help with early detection and efficient treatment of eye problems by being included into the healthcare system.