Scholarlink

Background <p>Diabetic Retinopathy (DR) is the leading cause of blindness in diabetics. It occurs when high blood sugar levels cause blood vessels to become blocked. There is a need for a hybrid deep learning framework that can detect DR early with high accuracy and generalization. The goal was to achieve high accuracy on a multi-class hybrid dataset using deep learning.</p> Methods <p>This study developed a novel hybrid model combining EfficientNetB0 with a Vision Transformer (ViT), preprocessed using CLAHE and Gaussian blur.</p> Results <p>The hybrid framework achieved an accuracy of 97.23%, and precision, recall f1 score and an AUC of more than 97%.</p> Conclusion <p>The interpretability techniques provided visual explanations of retinal characteristics for class predictions. Altogether, the hybrid framework outperformed previous studies and is highly suitable for application in real clinical settings.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Diabetic retinopathy detection enhancement using deep learning algorithms on hybrid dataset

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月30日

查看全文
  • Hamza Shahbaz,
  • Noman Ali

摘要

Background

Diabetic Retinopathy (DR) is the leading cause of blindness in diabetics. It occurs when high blood sugar levels cause blood vessels to become blocked. There is a need for a hybrid deep learning framework that can detect DR early with high accuracy and generalization. The goal was to achieve high accuracy on a multi-class hybrid dataset using deep learning.

Methods

This study developed a novel hybrid model combining EfficientNetB0 with a Vision Transformer (ViT), preprocessed using CLAHE and Gaussian blur.

Results

The hybrid framework achieved an accuracy of 97.23%, and precision, recall f1 score and an AUC of more than 97%.

Conclusion

The interpretability techniques provided visual explanations of retinal characteristics for class predictions. Altogether, the hybrid framework outperformed previous studies and is highly suitable for application in real clinical settings.