Early Detection of Glaucoma by Retinal Imaging
Received: 18 September 2025 | Revised: 18 December 2025 | Accepted: 19 December 2025 | Online: 9 February 2026
Corresponding author: K. R. Suma
Abstract
Glaucoma is a progressive and irreversible eye disease that leads to permanent vision loss if undetected and untreated. Early detection is crucial to mitigate its impact and prevent further deterioration of vision. Traditional methods of glaucoma diagnosis rely on manual inspection of retinal fundus images by ophthalmologists, which is both time-consuming and prone to subjective biases. This paper proposes an automated approach for glaucoma detection using retinal fundus images, leveraging the EfficientNetB3 Deep Learning (DL) architecture. The methodology integrates advanced preprocessing techniques such as green channel extraction, bilateral filtering, and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image quality. To address the issue of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is employed, ensuring the robustness of the model across different datasets. The proposed model is trained and evaluated on publicly available datasets, achieving significant performance metrics, including high precision and recall. This research demonstrates the potential of DL to aid in the early detection of glaucoma, offering a scalable, cost-effective solution for clinical applications.
Keywords:
glaucoma detection, retinal fundus images, Deep Learning (DL), EfficientNetB3, image preprocessing, Synthetic Minority Over-sampling Technique (SMOTE), data augmentationDownloads
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Copyright (c) 2025 K. R. Suma, Anandthirtha B. Gudi, Shridhar Kabbur
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