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Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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A Multi-Model Image Enhancement and Tailored U-Net Architecture for Robust Diabetic Retinopathy Grading.

Archana Singh1, Sushma Jain1, Vinay Arora1

  • 1Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India.

Diagnostics (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study presents an AI framework for diabetic retinopathy (DR) classification from retinal images. The system achieves over 99% accuracy, aiding early detection and preventing vision loss.

Keywords:
U-Netbiomedical image processingdata augmentationdeep learningdiabetic retinopathyimage classification

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Diabetic retinopathy (DR) is a primary cause of preventable vision loss in diabetic patients.
  • Early DR detection is crucial but challenging due to subtle progression and manual screening limitations.
  • This study addresses early diagnosis and fine-grained lesion identification in DR.

Purpose of the Study:

  • To introduce an AI-based framework for robust multiclass diabetic retinopathy classification.
  • To enhance early diagnosis and lesion discrimination in retinal fundus images.
  • To develop a scalable and interpretable solution for DR screening.

Main Methods:

  • A hybrid AI framework incorporating preprocessing, a Hybrid Local-Global Retina Super-Resolution (HLG-RetinaSR) module, and hierarchical classification.
  • The HLG-RetinaSR module combines deformable convolutional networks and vision transformers.
  • Classification utilizes a hierarchical approach with CNN, DenseNet-121, and a RefineNet-U architecture.

Main Results:

  • The HLG-RetinaSR and RefineNet-U approach achieved precision, recall, F1-score, and accuracy exceeding 99% across all DR severity levels.
  • The system effectively highlights vascular abnormalities and reduces background noise.
  • Performance surpassed existing state-of-the-art methods in accuracy and robustness.

Conclusions:

  • The proposed hybrid AI pipeline offers a scalable, interpretable, and clinically relevant solution for DR screening.
  • The system improves diagnostic reliability, supporting early intervention for diabetic retinopathy.
  • This AI tool has the potential to significantly assist ophthalmologists in reducing preventable vision loss.