Leveraging Deep Learning with Automated Fundus Imaging for Early Detection and Diagnosis of Eye Diseases

The most common causes of blindness and visual loss in the world are eye conditions such as diabetic retinopathy, age-related macular degeneration and glaucoma. Patients may benefit from the greatest outcomes if these illnesses are identified early and diagnosed. Fundus imaging helps in the diagnosis of eye disorders by providing detailed pictures of the optic nerve head as well as the retina. For feature extraction in normal, diabetic retinopathy, glaucoma, and macular degeneration fundus images, CNN was applied. A labeled fundus image dataset supports the training process. These datasets reverse the implications of the model not being generalizable to real world problems. Outcome is provided for a number of models including ResNet, VGG16, and designed custom CNN model. Model performance can be evaluated by means of F1-score, recall, accuracy, and precision metrics. The suggested method aims to provide an automated classification solution for eye diseases that is as scalable and effective as possible. Since early identification is vital for preventing permanent vision loss, this technique could prove beneficial in telemedicine and ophthalmology.