Ocular Disease Recognition Using Deep Learning

Ocular diseases are a global public health concern, demanding precise diagnostic methods. This study tackles the challenge of accurate recognition from low-resolution ocular images using a combined approach of Super-Resolution Generative Adversarial Networks (SRGAN) and Convolutional Neural Networks (CNN). Our method involves enhancing image quality through SRGAN to create high-resolution versions of low-quality ocular images. Subsequently, a CNN-based recognition model is trained on these enhanced images, learning discriminative features for precise classification of various ocular diseases. Performance evaluations on the Ocular disease dataset demonstrate the practicality and effectiveness of our approach. Beyond diagnostics, this SRGAN-CNN combination holds potential for telemedicine, enabling swift and reliable eye disease diagnosis, especially in remote areas. This synergy not only improves diagnostic accuracy but also streamlines analysis, enhancing treatment efficiency and patient outcomes.