Explainable AI-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.Author summary: Glaucoma is one of the leading causes of irreversible blindness worldwide, yet it often remains undetected until significant vision loss has occurred. In this study, we developed GlaucoXAI, an explainable artificial intelligence (AI) model that helps detect glaucoma early from retinal fundus images. Unlike conventional black-box AI systems, GlaucoXAI combines advanced image analysis with explainable AI methods to make its predictions more transparent and trustworthy to clinicians. The model uses a hybrid approach integrating feature extraction, dimensionality reduction, and optimized neural learning to enhance accuracy and speed. Tested on two publicly available datasets (G1020 and ORIGA), GlaucoXAI achieved over 93–95% accuracy, outperforming existing models. Importantly, it generates visual explanations that highlight regions of the eye most responsible for its predictions, supporting ophthalmologists in verifying AI-driven results. This work demonstrates that AI can be both accurate and interpretable, marking a step toward reliable clinical tools for early glaucoma diagnosis and better patient care.