Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection

Abstract

Glaucoma is a leading cause of blindness worldwide and results from high eye pressure-induced damage to the optic nerves, thereby preventing visual information from reaching the brain. While glaucoma is incurable in its advanced stages, its early detection improves the treatment outcome. Recently, computational models for image classification have enabled early detection of glaucoma from OCT scans of patients and will potentially augment the medical diagnosis of this disease. Models that are based on the convolutional neural networks (CNNs) have shown promise in the early detection of glaucoma. These models vary in their architectures and their accuracies depend largely on the intrinsic nature of the training datasets used. Hence, in this work, a comparative analysis is performed on vanilla CNN, AlexNet, GoogLeNet, and ResNet50 using two popular glaucoma datasets (ACRIMA and ORIGA). With careful attention to exhaustive image processing, an impressive training accuracy of 89% and validation accuracy of 50% was obtained from the vanilla CNN, showing a high sensitivity of 88% in detecting glaucomatous patients from OCT scans. However, the other models (AlexNet, GoogLeNet, and ResNet5) overfitted with a large difference between the obtained training accuracy and validation accuracy. The results also reveal that ResNet50 has the highest computational cost compared to the rest of the models. The obtained results demonstrate the peculiarity of the dataset, its selectiveness of the most appropriate model, and the potential of deep neural networks (DNNs) as an effective screening tool for glaucoma, enabling prompt interventions, reducing healthcare costs, and helping optometrists make swift decisions.