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Transformer-Based Deep Learning for Population-Scale Retinal Image Screening of Ophthalmic Disorders.

Wiem Abdelbaki¹, Wided Bouchelligua², Inzamam Mashood Nasir³

¹College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait.

Bioengineering (Basel, Switzerland)

|May 4, 2026

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This summary is machine-generated.

A new hierarchical transformer framework improves automated retinal screening for diseases like diabetic retinopathy. This AI model offers enhanced accuracy and generalizability for large-scale population health applications.

Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Population-scale retinal screening demands automated, accurate, and cost-effective models.
Current deep learning models struggle to balance fine-grained pathology and large-scale retinal context, impacting reliability.

Purpose of the Study:

To develop a novel hierarchical transformer-based framework for automated retinal fundus image screening.
To enhance the accuracy, robustness, and generalizability of AI models for detecting eye diseases.

Main Methods:

Proposed a hierarchical transformer framework with patch-based tokenization and global transformer encoding.
Implemented hierarchical aggregation of contextual information and a lightweight prediction head for multi-disease screening.
Evaluated the framework on EyePACS and RFMiD datasets using standard screening metrics, robustness, and cross-dataset generalization analyses.

Keywords:

diabetic retinopathy medical image analysis multi-disease classification population-scale healthcare retinal image screening vision transformers

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Main Results:

Achieved 89.4% accuracy and 93.6% AUROC for diabetic retinopathy screening on EyePACS.
Attained 95.2% accuracy and 82.7% F1 score on RFMiD.
Demonstrated superior robustness to image degradation and improved cross-dataset generalizability compared to state-of-the-art models.

Conclusions:

The hierarchical transformer framework shows significant potential for large-scale retinal screening.
The model offers a promising foundation for future clinical deployment in eye care.
The approach effectively integrates pathology and context for improved diagnostic performance.