Glaucoma is a progressive eye disease that can lead to irreversible vision loss if not detected and treated early. Elevated intraocular pressure, caused by impaired aqueous humor flow, damages the optic nerve and associated visual pathways. Routine eye examinations are essential for early diagnosis. Deep learning (DL) techniques have shown great potential in automating glaucoma detection from retinal fundus images with high accuracy and minimal expert intervention. This review presents a comprehensive analysis of recent DL-based approaches for glaucoma detection, covering image preprocessing, optic disc and cup segmentation, imaging modalities, benchmark datasets including ACRIMA, Drishti-GS1 and RIMONE, and evaluation metrics. The review further presents key findings and recommendations, and concludes by highlighting current challenges and future research directions to improve the clinical applicability and diagnostic performance of DL-based systems. Notably, several DL studies report over 98% accuracy, underscoring the promise of DL for reliable automated glaucoma diagnosis.
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