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Related Experiment Videos

A hybrid dual-stream CNN framework with dynamic data augmentation and improved Manta Ray Foraging Optimization for

Azza Atia1, Hatem Abdel-Kader2, Osama M Abo-Seida3

  • 1Department of Information Systems, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt. azzamohamed@fci.kfs.edu.eg.

Scientific Reports
|April 17, 2026
PubMed
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This study introduces an automated deep learning framework for early glaucoma detection, achieving high accuracy and precision. The innovative approach enhances diagnostic efficiency and aids in preventing irreversible blindness.

Area of Science:

  • Ophthalmology
  • Computer Science
  • Artificial Intelligence

Background:

  • Glaucoma is a leading cause of irreversible blindness globally.
  • Early detection is critical for preventing vision loss.
  • Conventional diagnostic methods are time-consuming and require specialized expertise.

Purpose of the Study:

  • To develop an automated deep learning framework for glaucoma detection.
  • To address challenges like data imbalance, image quality, and feature extraction.
  • To improve the efficiency and accuracy of glaucoma screening.

Main Methods:

  • A deep learning framework with a preprocessing pipeline for image enhancement.
  • A hybrid data augmentation strategy to handle class imbalance.
  • A dual-stream CNN integrating DenseNet121 and ResNet50 with a channel-wise attention mechanism.
Keywords:
Dual-stream convolutional neural network (CNN)Dynamic Gaussian noiseGlaucoma detectionLightweight channel-wise attention mechanism

Related Experiment Videos

  • Hyperparameter optimization using an Improved Manta Ray Foraging Optimization (IMRFO) algorithm.

    • Main Results:

    • The framework demonstrated superior performance on four public datasets (ACRIMA, Drishti-Gs, ORIGA, RIM-ONE-DL).
    • Achieved 100% accuracy, precision, recall, and AUC on ACRIMA and Drishti-Gs.
    • Scored high performance on ORIGA (99.70% accuracy) and RIM-ONE-DL (99.90% accuracy).

    Conclusions:

    • The proposed deep learning framework is robust and effective for glaucoma screening.
    • It offers a promising solution for automated, efficient, and accurate early glaucoma detection.
    • The study highlights the clinical applicability of advanced AI in ophthalmology.