Enhanced Cataract Prediction Using VGG19 Deep Learning Model

Globally, cataracts are the main cause of vision loss; they are marked by the lens in the eye clouding that greatly influences visual acuity. Early cataract diagnosis and treatment are essential to prevent significant vision loss and enhance patient quality of life, therefore promoting world health and well-being. Using the Ocular Disease Intelligent Recognition (ODIR) dataset, we build a model in this work that correctly distinguishes retinal pictures as either Normal or Cataract. The dataset comprises color fundus images and diagnostic keywords, therefore guaranteeing a varied collection of photos taken with different cameras and hence ensuring durability in real-world conditions. We trained our model on 876 photos and verified it on 218 images using VGG19, a convolutional neural network distinguished for depth and performance, obtaining an amazing accuracy of 98%. This great accuracy shows the possibilities of our model to help in early cataract identification, so boosting excellent healthcare and supporting personal well-being, so advancing worldwide health projects.