A Lightweight Vision Transformer and Retinal Biomarker Fusion Framework for Early Glaucoma Detection: Toward Improved Clinical Screening.

Background/Objectives: Glaucoma is a leading cause of irreversible blindness, and it is hard to catch early be-cause it rarely causes symptoms until real damage has already occurred. Existing au-tomated detection methods still miss the subtle structural changes that show up before vision loss begins. This study presents an automated framework based on optic nerve structure and retinal biomarkers, aimed at enabling earlier and more consistent glau-coma screening. The framework combines segmentation and classification in a single deep learning pipeline, trained and tested on the ORIGA and REFUGE2 fundus image datasets. Each image is resized, denoised with median filtering, and contrast-enhanced through histogram equalisation and normalisation, then cropped down to the optic nerve region using central cropping and intensity-based localisation. Methods: A custom encod-er-decoder CNN segments the optic disc and cup, and from that segmentation, we ex-tract biomarkers ophthalmologists already rely on, including the vertical cup-to-disc ratio and disc/cup area measurements. A Lightweight Vision Transformer separately learns broader structural patterns across the retina, and the two feature sets are fused and passed through a SoftMax classifier. Results: Segmentation accuracy was strong: Dice scores of 0.9082 for the optic disc and 0.9994 for the optic cup, IoU scores of 0.8351 and 0.9988, and an overall mean Dice of 0.9538 and mean IoU of 0.9169. Classification performance held up well, too, with high F1-score, recall, accuracy, and precision across normal and glaucomatous cases. Conclusions: The combination of interpretable, clinically established biomarkers with transformer-based global feature learning gives the framework the ability to support automated glaucoma risk assessment and demon-strates promising performance for glaucoma detection using retinal fundus images.