Publication

Abstract : Eye diseases occur when the tissues around the cornea and other parts of the eye are damaged. Some may be minor which doesn’t have much impact on the vision and if the damage is severe it may lead to vision loss. Early detection of such damage of tissue needs to be addressed. This paper proposes a revolutionize eye care with a real-time eye disease classification system accessible via smartphones. Using a powerful convolutional neural network (CNN) the proposed model identifies quickly and accurately cataracts, diabetic retinopathy, glaucoma, and normal eyes. Developed and tested in PyCharm, this model ensures top-notch performance. The cloud-based interface, built with Streamlit, offers a user-friendly and interactive experience for both healthcare professionals and patients. This innovative solution makes it easy to classify eye diseases on the spot, especially benefiting those in remote or underserved areas. This innovative technology is designed to make high-quality eye care accessible to everyone, regardless of their location. By incorporating advanced deep learning techniques, our application will analyze eye fundus images in real-time, ensuring accurate and prompt diagnosis. This approach not only helps in detecting eye diseases at an early stage but also empowers healthcare providers to offer better and more efficient care to the patients.