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Z ALPHA MULTIDISCIPLINARY RESEARCH JOURNAL (ZAMRJ)

e-ISSN: 3139-0846

ZAMRJ, Vol. 2, Issue 1, 2026 (Regular Issue)

Earlier Glaucoma Detection Using Optimized Optic Disc and Optic Cup Segmentation with CNN-Based Deep Learning Framework

Article Type: Research Article

Article History: Received: 21-May-2026; Revised: 14-Jun-2026; Accepted: 15-Jun-2026; Published Online: 23-Jun-2026

Authors: Hariharan K a* | G. Geetharamani a | Poonggazhal TTK a | G. Amuthavalli a | T. Jenifer a | S. Shreya a | M. Bhuvaneswari a

a Department of Computer Science Engineering, MIET Engineering College, Tamil Nadu, India.

* Author for Correspondence Email: hariarulmeera1@gmail.com

Abstract

Glaucoma, being one of the major causes of irreversible blindness, is largely asymptomatic in its early stages, and therefore early detection is the hour of need. Traditional diagnostic techniques are time-consuming and are prone to variability. In response to this problem, we introduce a deep learning-based approach that efficiently segments the glaucoma-sensitive Optic Disc (OD) and Optic Cup (OC) using an ensemble of Convolutional Neural Networks (CNNs). To ensure increased accuracy of predictions, the system uses majority voting and state-of-the-art Attention U-Net models with pre-trained ResNet34, Inceptionv3, and DenseNet121 backbones. Following training of the ensemble over a heterogeneous dataset of REFUGE, Drishti-GS, and RIM-ONE images, the ensemble outperforms in glaucoma classification and fundus image segmentation. The novelty lies in the complementary use of ensemble learning and backbone-specific Attention U-Nets, which improve segmentation accuracy and reduce model over fitting. Traditional preprocessing methods, Gaussian noise removal, and CNN-based classification pipeline further enhance the accuracy of predictions. The system finally offers an efficient and automated way to enable early glaucoma screening and diagnosis.

Keywords: Glaucoma Detection, Retinal Imaging, Fundus Analysis, Medical Screening, Image Segmentation, Visual Impairment, Blindness Prevention, Eye Disease, Clinical Decision Support.

How to Cite this Article: Hariharan, K., Geetharamani, G., Poonggazhal, T. T. K., Amuthavalli, G., Jenifer, T., Shreya, S., & Bhuvaneswari, M. (2026). Earlier glaucoma detection using optimized optic disc and optic cup segmentation with CNN-based deep learning framework. Z Alpha Multidisciplinary Research Journal, 2(1), 14–26.

How to Cite this URL: Hariharan, K., Geetharamani, G., Poonggazhal, T. T. K., Amuthavalli, G., Jenifer, T., Shreya, S., & Bhuvaneswari, M. (2026). Earlier glaucoma detection using optimized optic disc and optic cup segmentation with CNN-based deep learning framework. Z Alpha Multidisciplinary Research Journal, 2(1), 14–26. Available from: https://www.zalpharph.in/volume2%20issue1/ZAMRJ9.pdf

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