Eye Disease Prediction based on Deep learning Approaches

Abstract

Glaucoma (GC), diabetic retinopathy (DR), and cataracts are the leading causes of vision loss globally. However, the diseases can be prevented from further progression when detected in the early stages. Conventional manual diagnosis is prolonged and requires the experience of a trained ophthalmologist. Therefore, automated techniques using artificial intelligence algorithms are evaluated for their ability to identify these eye diseases. In this study, transfer learning and image augmentation techniques are employed using pre-trained deep-learning (DL) neural network (EfficientNetB3). The model tested based on public available dataset known as from Kaggle repository. The model attained an accuracy of 95%. The knowledge from this study has the potential to aid, hasten, and improve accuracy in the process of eye disease prediction.