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April 12, 2026International Journal of Pattern Recognition and Artificial Intelligence

A Deep-learning based hybrid model for glaucoma classification using Cup-to-Disc Ratio

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Authors

AAAlzubair AlgaraghuliSOSerkan OzturkErciyes University

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Implication

Demonstrates a deep learning model that classifies glaucoma in diverse datasets, indicating an efficient diagnostic tool for eye health.

Key Points

  • The study aims to develop and evaluate a deep learning model for automated glaucoma detection based on optic disc and cup segmentation.
  • Introduced a dual-network architecture using hybrid CNNs for segmentation tasks.
  • Performed evaluations using benchmark datasets RIM-ONE, ACRIMA, and Drishti-GS.
  • Measured accuracy and segmentation scores with metrics like Dice coefficient.
  • Achieved glaucoma prediction accuracy of 96.42%, 93.2%, and 94.4% on RIM-ONE, ACRIMA, and Drishti-GS, respectively.
  • Obtained cup/disc segmentation scores of 90/97%, 90/95%, and 94/97% for the same datasets.
  • Demonstrated robustness by comparing with recent methods across varying architectures.

Cite This Study

Algaraghuli et al. (2026) studied this question.

synapsesocial.com/papers/69db37b04fe01fead37c5b6chttps://doi.org/10.1142/s0218001426400094
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