Deep Learning-Based Ocular Disease Recognition

Abstract

This study investigates the use of image classification methods to identify eye disorders using fundus images. Ocular disorders can significantly affect a person's quality of life, but frequent eye exams can help identify them early and prevent vision loss. Manual diagnosis, however, can take a while and is prone to mistakes made by humans. This study suggests utilising deep learning methods to automatically identify eye disorders from fundus photos. To classify several eye disorders, such as age-related macular degeneration, cataracts, and glaucoma, a convolutional neural network (CNN) model is created and trained using a sizable dataset of fundus images. The proposed CNN model achieves high accuracy in the classification of ocular diseases, demonstrating the potential of automated diagnosis for early detection and prevention of vision loss. The results of this research indicate that image classification techniques can significantly improve the accuracy and speed of ocular disease recognition, paving the way for improved ocular health management.