Explainable Al-driven diagnosis model for early glaucoma detection using grey-wolf optimized extreme learning machine approach

Glaucoma is a leading global cause of blindness, making early detection essential. This paper introduces GlaucoXAI (Glaucoma Explainable AI), an advanced computer-aided diagnosis (CAD) model that integrates machine learning and explainable AI for glaucoma detection using retinal fundus images. The proposed model consists of four stages, including preprocessing, feature extraction, dimensionality reduction, and classification. Initially, features are extracted using the fast discrete curvelet transform with wrapping (FDCT-WRP) to obtain curve-type features. During the next stage, principal component analysis (PCA) and linear discriminant analysis (LDA) are combined to reduce the dimensionality of the feature matrix, followed by a classification stage employing an improved grey wolf optimization (IMGWO) with an extreme learning machine (ELM) to optimize the weight and bias to reduce the overfitting of the model. The model has been experimented with two publicly available datasets named G1020 and ORIGA. The model has achieved 93.87% accuracy on G1020 and 95.38% on ORIGA, outperforming existing methods. The 10 × 5-fold stratified cross-validation (SCV) with explainable AI enhances the interpretability of models and improves clinician trust. Overall, the proposed approach offers accurate, efficient, and explainable glaucoma diagnosis, potentially supporting ophthalmologists in early disease detection.