Gradient Based Insights Into Eye Disease Identification With AI

Eye diseases represent a significant public health challenge, particularly in regions with limited access to specialized medical care. In India, where millions suffer from curable blindness, the need for early detection and intervention is critical. This paper proposes a novel approach leveraging deep learning techniques, including Convolutional Neural Networks (CNNs) such as ResNet, VGG16, and VGG19, to automate the identification of eye diseases based on visually observable symptoms. By analyzing digital images of eyes, our model categorizes and detects conditions. The integration of CNNs enables accurate and efficient analysis, facilitating early disease detection and prompting patients to seek timely medical attention. This automated system holds promise for improving healthcare outcomes and reducing the burden of preventable blindness worldwide.