E-GlauNet: A CNN-Based Ensemble Deep Learning Model for Glaucoma Detection and Staging Using Retinal Fundus Images
Anwar M. Farhan S. Ul Haq Y. Azeem W. Ilyas M. Voicu R.C. Tanveer M.H.
2025Tech Science Press
Computers, Materials and Continua
2025#84Issue 23477 - 3502 pp.
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. Copyright
Classification , deep learning , early disease detection , ensemble learning , glaucoma , machine learning , retinal fundus images
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Department of Computer Science, Lahore College for Women University, Lahore, 44444, Pakistan
Department of Computer Science and Engineering, University of Engineering and Technology Lahore, Narowal Campus, Narowal, 51600, Pakistan
Department of Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom
School of IT & Engineering (SiTE), Kazakh-British Technical University, Almaty, 050005, Kazakhstan
Department of Robotics and Mechatronics Engineering, Kennesaw State University, Marietta, 30060, GA, United States
Department of Computer Science
Department of Computer Science and Engineering
Department of Computer Science
School of IT & Engineering (SiTE)
Department of Robotics and Mechatronics Engineering
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