Cataract Prediction with VGG19 Architecture Using the Ocular Disease Dataset

Ocular diseases, particularly cataracts, pose significant challenges to global eye health, necessitating accurate and timely diagnostic approaches. This study aims to enhance the identification of eye diseases by applying deep learning techniques. It specifically focuses on predicting cataracts using the VGG19 architecture. By utilising a portion of the Ocular Disease Intelligent Recognition (ODIR) database, which consists of 588 cataract images and 1088 normal photos, we implement meticulous data pretreatment approaches to guarantee the integrity and variety of the data. The VGG19 convolutional neural network (CNN) architecture is selected because of its established effectiveness in image categorisation tasks. Additionally, the model is fine-tuned using transfer learning techniques to optimise its performance specifically for cataract prediction. Through comprehensive training and evaluation procedures, the developed model achieves an impressive accuracy of 98.62%, demonstrating its robustness and reliability in cataract detection. The findings of this study contribute significantly to advancing ocular disease recognition, with potential implications for early disease diagnosis, personalised treatment planning, and improved patient outcomes. This study underscores the importance of leveraging cutting-edge AI techniques in addressing critical healthcare challenges and underscores the potential of deep learning in revolutionising ophthalmic care.