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March 14, 2026Journal of Radiation Research and Applied SciencesOpen Access

A hybrid deep learning approach for efficient detection and classification of internal eye diseases

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Authors

LHLoay F. HusseinIAIslam Abdalla Mohamed AbassIAIbrahim Alrashdi

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Overview

This research demonstrates improved glaucoma detection accuracy in low-resource settings, suggesting a scalable solution.

Key Points

  • The aim is to enhance the detection and classification of glaucoma using a hybrid deep learning approach.
  • Proposed a hybrid architecture combining CNN for spatial feature extraction and handcrafted descriptors.
  • Utilized Gated Recurrent Unit to model temporal dynamics.
  • Implemented the maximum relevance minimum redundancy method for feature optimization.
  • Classified data using multiple machine learning classifiers including MLP, GRU, SVM, RF, and KNN.
  • Tested on publicly available glaucoma fundus image datasets.
  • Achieved 99.7% accuracy in glaucoma detection.
  • Reported 99.6% precision and 99.5% recall.
  • F1-score reached 99.55%, surpassing existing methods.

Cite This Study

Hussein et al. (2026) studied this question.

synapsesocial.com/papers/69b4faf0b39f7826a300b8f7https://doi.org/10.1016/j.jrras.2026.102288
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