Deep Learning -Based System for Early Detection of Ocular Diseases

The Ocular Disease Detection project aims to automate the diagnosis of various eye diseases from fundus images using deep learning techniques. The project leverages the dataset, which includes fundus images of patients diagnosed with conditions such as Diabetic Retinopathy, Glaucoma, Cataract, and others. The project utilizes ResNet50 architecture for classification. The system is trained to classify the images based on the presence of diseases, improving its accuracy over multiple epochs. Evaluation parameters like training accuracy, training loss, validation accuracy, and validation loss are used to assess the model's performance. By the end of the project, the trained model can predict ocular diseases from fundus images, providing an automated solution for faster and more reliable diagnoses. Keywords - Automated diagnosis, Convolutional Neural networks (CNN), Eye health monitoring, Neural network classification, Ophthalmology AI, Retinal image processing, Image-based disease detection, Vision diagnosis system.