Performance Assessment of EyeNet Model in Glaucoma Diagnosis

Deep learning (DL) has recently gained increasing attention in biomedical data analytics, demonstrating robust performance and promising results. A deep network requires massive amount of data to learn meaningful patterns useful in solving complex problems. Data scarcity in medical field is a bottleneck for applying deep learning in this area. This has led to the popularity of pre-trained models, trained on huge source data to achieve reasonable accuracy in medical diagnosis even with less data in target domain. A wise choice of models trained with data similar to target data would ensure that relevant features are captured. In this work, the significance of choosing appropriate pre-trained models is demonstrated. The EyeNet model, originally trained for diagnosis of diabetic retinopathy (DR) using fundus image dataset, is used as a pre-trained model for building a convolutional neural network (CNN) – based DL architecture for glaucoma diagnosis using images from the same modality. The results are compared with glaucoma diagnosis using different pre-trained models that are less relevant to the problem considered. Different experiments including fine-tuning and transfer learning were performed. Results were validated using the benchmark Rim-one dataset. The EyeNet model outperformed all other models, achieving a maximum accuracy of 89% with transfer learning using support vector machines (SVM) combined with principal component analysis (PCA) for dimensionality reduction.