Cataract Disease Detection Using Pre-trained Models

Abstract

Early detection and prevention of Cataract disease can effectively contribute in reducing the impact of cataracts. In this study, we explore the effectiveness of deep learning algorithms implemented with three pre-trained models —MobileNet VGG19, and ResNet50— for cataract disease detection. These algorithms leverage image processing techniques and have shown promise in various computer vision tasks. Our objective is to predict which algorithm performs best in cataract detection. We use a dataset of retinal fundus images to train and evaluate the models. The results demonstrate the potential of deep learning in early cataract diagnosis, which can significantly improve patient outcomes. Our model was able to achieve an accuracy of 96.33%.