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New research · Ophthalmology
Frontiers in digital health · 23h
AI / informaticsFrontiers in digital health · 2026

Transfer learning and vision transformer for the automatic diagnosis of cataracts in ophthalmological images.

Hugo Vega-Huerta, Camila Isabela Cuba-Aquino, Gari Mario Suca-Mariño … Javier Elmer Cabrera-Díaz
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OphthalmologyAI / informatics

Automated deep learning system accurately detects cataracts in retinal images.

Transfer learning and vision transformer for the automatic diagnosis of cataracts in ophthalmological images.

Hugo Vega-Huerta et al. · Frontiers in digital health · 2026
Background

Cataracts continue to be the leading cause of preventable blindness worldwide and represent a significant public health challenge, particularly in rural and underserved regions where access to ophthalmology specialists and diagnostic infrastructure is limited.

Purpose

The purpose of this research was to develop and evaluate an automated cataract detection system based on deep learning using retinal fundus images in order to support early screening and improve accessibility to ophthalmological diagnosis.

Methods

The proposed methodology followed an experimental quantitative approach that included dataset preparation, image preprocessing, model training, and performance evaluation.

n = 4,840 images
99.10%
Results
The system was correct in almost all its assessments of cataracts from eye scans
n = 4,840 images
More results

It is concluded that deep convolutional neural network architectures, particularly ResNet152, provide highly effective performance for automated cataract detection from retinal fundus images.

“
Conclusion

DISCUSION: The proposed system demonstrates strong potential as a clinical decision-support tool for large-scale screening programs, especially in resource-limited settings, as it can facilitate early diagnosis, improve access to ophthalmological care, and reduce the diagnostic workload of specialized medical personnel.

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