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September 5, 2025UHD Journal of Science and TechnologyOpen Access

Enhancing Clinical Decision Support: A Deep Learning Approach for Automated Diagnosis of Eye Diseases from Fundus Images

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Authors

MIMuhammad Ali IqbalSMSozan Abdulla Mahmood

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Overview

Hybrid CNN models enhance accuracy for retinal condition classification, suggesting improvements for clinical decision support.

Key Points

  • The hybrid deep learning model improved accuracy and precision for classifying eye diseases.
  • Performance metrics reached 98.77% for area under the curve on the Kaggle dataset for eye disease classification.
  • Various CNN architectures were evaluated, with DenseNet169 and MobileNetV1 yielding the best results.
  • Automated detection of retinal diseases may streamline clinical workflows and enhance patient care.

Cite This Study

Iqbal et al. (2025) studied this question.

synapsesocial.com/papers/68bb3a3d2b87ece8dc9552bdhttps://doi.org/10.21928/uhdjst.v9n2y2025.pp61-76
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Also Consider

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  1. 1Deep Learning-Based Multi-Class Classification Approach for Diabetic Eye Diseases2026
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  4. 4The classification of eye diseases from fundus images based on CNN and pretrained models2024 · 3 citations
  5. 5Early retinal disease detection from fundus images using deep neural networks2026 · 1 citations