VisionCare: AI-Driven Macular Degeneration Diagnosis using Fundus Images

One of the main causes of vision loss and blindness, particularly in older persons, is macular degeneration. To stop serious sight decline, early detection is essential. The goal of this project is to create an automated system that uses fundus images to diagnose macular degeneration. Deep learning techniques will be used to improve the diagnostic' speed and accuracy. Fundus imaging is the best modality for identifying retinal abnormalities associated with macular degeneration because it offers detailed views of the retina. We suggest training VGG16 Convolutional Neural Networks (CNNs) on publicly accessible datasets of labelled fundus images in order to classify images. The model will be built to discriminate between retinal images that are normal and those that exhibit early, moderate, and severe stages of macular degeneration. The system will use data preparation methods like picture augmentation and normalization to improve performance. Classification metrics, such as accuracy, precision, recall, and AUC (Area Under the Curve), will be used to assess the final model. By offering rapid, accurate, and non-invasive screening for macular degeneration, this technology may help ophthalmologists by promoting early intervention and better patient outcomes.