E-GlauNet: A CNN-Based Ensemble Deep Learning Model for Glaucoma Detection and Staging Using Retinal Fundus Images

Abstract

Glaucoma, a chronic eye disease affecting millions worldwide, poses a substantial threat to eyesight and can result in permanent vision loss if left untreated. Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective. To address these challenges, this research proposes a computer-aided diagnosis (CAD) approach using Artificial Intelligence (AI) techniques for binary and multiclass classification of glaucoma stages. An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network (ConvNet) models–ResNet-50, VGG-16, and InceptionV3 is utilized in this paper. This fusion technique enhances diagnostic accuracy and robustness by ensemble-averaging the predictions from individual models, leveraging their complementary strengths. The objective of this work is to assess the model’s capability for early-stage glaucoma diagnosis. Classification is performed on a dataset collected from the Harvard Dataverse repository. With the proposed technique, for Normal vs. Advanced glaucoma classification, a validation accuracy of 98.04% and testing accuracy of 98.03% is achieved, with a specificity of 100% which outperforms state-of-the-art methods. For multiclass classification, the suggested ensemble approach achieved a precision and sensitivity of 97%, specificity, and testing accuracy of 98.57% and 96.82%, respectively. The proposed E-GlauNet model has significant potential in assisting ophthalmologists in the screening and fast diagnosis of glaucoma, leading to more reliable, efficient, and timely diagnosis, particularly for early-stage detection and staging of the disease. While the proposed method demonstrates high accuracy and robustness, the study is limited by the evaluation of a single dataset. Future work will focus on external validation across diverse datasets and enhancing interpretability using explainable AI techniques.