Revealing the Insights: Interpretable Transfer Learning for Diagnosing Eye Disorders - Takeaways - MDSpire
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Revealing the Insights: Interpretable Transfer Learning for Diagnosing Eye Disorders

  • By

  • Zaib un Nisa

  • Arfan Jaffar

  • Sohail Masood Bhatti

  • Ines Hilali Jaghdam

  • Tehseen Mazhar

  • Muhammad Amir Khan

  • Habib Hamam

  • February 1, 2026

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  • 1

    Ocular disorders are a leading cause of blindness, affecting 2.2 billion people globally, necessitating early detection for better outcomes.

  • 2

    This study compares eight transfer learning models for diagnosing cataract, diabetic retinopathy, and glaucoma, highlighting disease-specific performance.

  • 3

    Explainable AI techniques like Grad-CAM, LIME, and SHAP are utilized to provide multilevel interpretability for the transfer learning models.

  • 4

    The research emphasizes the importance of interpretability in AI models, aiding medical professionals in making informed decisions.

  • 5

    Previous studies focused on single diseases; this study offers a comprehensive evaluation of multiple diseases using explainable AI.

Original Source(s)

Revealing the Insights: Interpretable Transfer Learning for Diagnosing Eye Disorders

  • by Zaib un Nisa, Arfan Jaffar, Sohail Masood Bhatti, Ines Hilali Jaghdam, Tehseen Mazhar, Muhammad Amir Khan, Habib Hamam

  • February 1, 2026

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