Ensemble-Based Deep Learning Framework for Glaucoma Detection in Retinal Fundus Images

Artificial Intelligence (AI) has shown significant potential in healthcare, particularly for the early detection of glaucoma. This study presents a comprehensive evaluation of glaucoma classification using the ACRIMA dataset. Handcrafted descriptors, such as Edge Histogram (EH), Fuzzy Color and Texture Histogram (FCTH), and Pyramid Histogram of Gradients (PHOG), are combined with deep features extracted from ConvNeXt and Vision Transformer (ViT) models, and their classification performance is evaluated using three different classifiers, namely Random Forest (RF), Gradient Boosting (GB), and LightGBM (LGBM). Four ensembling frameworks, namely Feature-level (FL), Hybrid Feature-level (HF), Voting-based (VB), and Stacking ensembles, are investigated to integrate handcrafted and deep features effectively. Among the proposed approaches, the stacking ensemble using Logistic Regression (LR) as a meta-classifier outperformed other approaches by providing an accuracy of 95.44404% with an F1-score of 96.0638% and an AUC of 0.9887.