EYE DISEASE PREDICTION AND DIAGNOSIS

The advancement of deep learning has ushered in a new era in medical imaging and diagnostics. This paperexplores a comprehensive approach to the prediction and diagnosis of eye diseases using state-of-the-artdeep learning algorithms. By leveraging retinal imaging data and novel convolutional neural networkarchitectures, the study aims to enhance early detection and classification of conditions such as diabeticretinopathy, glaucoma, and strabismus. The methodology involves preprocessing of ultra-widefield fundusimages, application of transfer learning, and rigorous performance evaluation using standard metrics.Results indicate that the proposed approach not only improves diagnostic accuracy but also reducescomputational complexity, thereby offering a viable solution for telemedicine applications. Future workwill explore integration with electronic health records and real-time diagnostic systems. The researchpresents an innovative framework that could significantly influence both clinical practices and thedevelopment of automated diagnostic tools