Attention based multi-scale edge-aware segmentation and convolutional transformer framework for automated glaucoma...

BackgroundGlaucoma is a leading cause of irreversible vision loss and is characterized by subtle structural changes in the optic disc and optic cup. However, existing automated detection systems often suffer from weak boundary delineation, dataset variability, and unstable feature learning, which limit their generalizability and clinical reliability.ObjectiveThis study aims to develop a unified and anatomically guided framework for accurate and reliable automated glaucoma detection from fundus images.MethodsThe proposed pipeline begins with contrast-enhanced preprocessing to improve image quality, followed by an Attention-guided Multi-scale Edge-aware Segmentation Network (AME-SegNet) for precise segmentation of the optic disc and optic cup. Both deep convolutional features and clinically relevant geometric features are extracted and optimized using Bitterling Colony Optimization (BCO) to select the most discriminative attributes. A Convolutional Transformer (CT) is then employed to integrate local convolutional representations with global attention mechanisms for robust classification. Additionally, the Honey Badger Algorithm (HBA) is used for automatic parameter tuning to ensure stable convergence.ResultsExperimental evaluation demonstrates high segmentation performance with Dice scores of 97.36% for the optic disc and 96.72% for the optic cup on the Drishti-GS1 dataset. The classification model achieves accuracies of 98.63% on RIM-ONE and 98.96% on ORIGA-Light datasets, indicating strong generalization capability.ConclusionsThe proposed framework exhibits robust performance, high accuracy, and strong generalization across multiple datasets. These results highlight its effectiveness and clinical potential for reliable automated glaucoma screening and early diagnosis.