Transfer learning and hybrid models for the classification of eye diseases on retinal images

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 featurization techniques are employed using pre-trained deep-learning (DL) neural networks (ResNet50, MobileNetV2, and VGG16) and statistical machine learning (ML) algorithms (MLP Classifier, KNN, and Random Forest Classifier). These architectures were trained, validated, and tested using a public dataset that included retinal images for diseased (GC, DR, and cataracts) and normal eyes. The ResNet50 neural network architecture had the highest testing accuracy of 91.51% among the deep learning methods. Due to its high performance, features were extracted from this model (featurization) and used for the statistical ML classifiers, creating hybrid models. The MLP Classifier hybrid model had the highest accuracy value at 92.04%. The knowledge from this study has the potential to aid, hasten, and improve accuracy in the process of eye disease diagnosis.