Ocular Disease Detection WebApp Millions of people around the world suffer from various ocular diseases often leading to blindness due to delayed detection and treatment. This has led to a demand for quick automated detection process from medical images including retinal fundus images. Some efficient deep convolutional neural network (CNN) based architectures are implemented utilizing the pre-trained weights obtained via transfer learning. Convolutional neural network (CNN) has achieved remarkable success in the field of fundus images due to its powerful feature learning ability. Computer-aided diagnosis can obtain information with reference value for doctors in clinical diagnosis or screening through proper processing and analysis of fundus images In this model the fundus images based on CNN to directly detect one or more fundus diseases in the retinal fundus images. Every single model consists of two parts. The first part is a feature extraction network based on Resnet, and the second part is a custom classification neural network for multi-label classification problems

• Helps in Faster Detection of diseases
• Identifies Eye Disease
• Web Based User Interface