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<p>Glaucoma is the major cause of blindness among people across the globe. For automated, accurate and faster detection of glaucoma, Deep Learning (DL) techniques are used. This study proposes a novel DL hybrid model named RDI-Glaucoma which combines ResNet, DenseNet, and InceptionNet architectures for improving the glaucoma detection accuracy. This improves diagnostic accuracy with enhanced reliability in clinical settings. For the Drishti-GS dataset, this model integrates the outputs from ResNet, DenseNet, and InceptionNet to achieve maximized performance of various metrics such as precision, recall, and F1-scores. The RDI-Glaucoma model outperformed and achieved better accuracy, precision, recall, and F1-score for glaucoma detection.</p>

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Enhancing Automated Glaucoma Diagnosis with a Hybrid RDI-Glaucoma Model: A Performance Assessment Study

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • Tushar Verma,
  • Jasminder Kaur Sandhu,
  • Sachin Ahuja

摘要

Glaucoma is the major cause of blindness among people across the globe. For automated, accurate and faster detection of glaucoma, Deep Learning (DL) techniques are used. This study proposes a novel DL hybrid model named RDI-Glaucoma which combines ResNet, DenseNet, and InceptionNet architectures for improving the glaucoma detection accuracy. This improves diagnostic accuracy with enhanced reliability in clinical settings. For the Drishti-GS dataset, this model integrates the outputs from ResNet, DenseNet, and InceptionNet to achieve maximized performance of various metrics such as precision, recall, and F1-scores. The RDI-Glaucoma model outperformed and achieved better accuracy, precision, recall, and F1-score for glaucoma detection.