Authors

P.Revathi

Research Scholar, Department of Computer Science, NGM College, Pollachi, Tamilnadu, India

Dr. R.Jayaprakash

Assistant Professor, Department of Computer Science, NGM College, Pollachi, Tamilnadu, India

Abstract

Over the Decade, The machine learning incorporated on image classification has gained increasing attention for extracting discriminative patterns from retinal fundus images and supporting automated glaucoma severity analysis respectively. The several studies have explored convolutional neural networks, feature extraction, support vector machines, Random Forest, and ensemble learning strategies for glaucoma classification. The study proposes a novel weighted ensemble machine learning framework integrating LightGBM for glaucoma severity classification. The proposed framework begins with preprocessing of retinal fundus images using contrast enhancement, normalization, and noise reduction. Multiple feature representations, including structural, texture, morphological, and CNN-based deep features, are generated to capture complementary characteristics of the input images. The features are provided to multiple machine learning classifiers to obtain individual class-probability predictions respectively. A performance-based weighted ensemble mechanism is needed to assign differentiated weights to the classifier outputs according to their predictive contribution. Providing an enhanced computational framework for automated glaucoma severity classification respectively.

Keywords

XGBoost Glaucoma Severity Classification Retinal Fundus Images Feature Extraction Deep Features Decision Fusion CNN Classification

Citation of this Article

P.Revathi, & Dr. R.Jayaprakash. (2026). An Efficient Glaucoma Prediction Using an Optimized CNN and XGBoost with Attention-Based Deep Learning Framework. Current Journal of Engineering and Science Research. 3(7), 22-28. Article DOI: https://doi.org/10.47001/CJESR/2026.307003 

Licence Copyright (c) 2026 Current Journal of Engineering and Science Research. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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