Scholarlink

Background <p>Diabetic retinopathy is a major cause of visual impairment worldwide and a growing public health concern due to the rising prevalence of diabetes. Timely and accurate detection is critical to preventing irreversible vision loss.</p> Objective <p>This study proposes DcareNet, a novel deep learning algorithm designed to improve the robustness, accuracy, and efficiency of diabetic retinopathy classification.</p> Methods <p>DcareNet was developed to automatically detect key retinal biomarkers such as microaneurysms, exudates, and abnormal blood vessels. The model integrates advanced feature extraction techniques and data optimization strategies to overcome challenges related to class imbalance, poor image quality, and limited datasets. Performance was evaluated using standard diabetic retinopathy datasets under controlled experimental conditions.</p> Results <p>DcareNet achieved a classification accuracy of 94.4% with an error rate of 5.58%, outperforming traditional approaches. These results highlight the mode’s reliability and potential for real-world clinical application, particularly in environments with limited ophthalmological resources.</p> Conclusion <p>DcareNet provides a scalable, cost-effective solution for diabetic retinopathy screening. It supports early intervention and bridges the gap between diagnostic demand and medical expertise in both developed and resource-constrained settings. The proposed approach aligns&#xa0;with the United Nations Sustainable Development Goals, specifically Goal 3 (Good Health and Well-being) and Goal 10 (Reduced Inequality), by promoting accessible, high-quality healthcare.</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

Detection and classification of diabetic retinopathy using DcareNet

  • 期刊论文

  • 发表日期：

    2025年11月17日

查看全文
  • Priya Govindarajan,
  • H. M. Chandan,
  • Aniketh P. Vernekar,
  • Yasir Hamid

摘要

Background

Diabetic retinopathy is a major cause of visual impairment worldwide and a growing public health concern due to the rising prevalence of diabetes. Timely and accurate detection is critical to preventing irreversible vision loss.

Objective

This study proposes DcareNet, a novel deep learning algorithm designed to improve the robustness, accuracy, and efficiency of diabetic retinopathy classification.

Methods

DcareNet was developed to automatically detect key retinal biomarkers such as microaneurysms, exudates, and abnormal blood vessels. The model integrates advanced feature extraction techniques and data optimization strategies to overcome challenges related to class imbalance, poor image quality, and limited datasets. Performance was evaluated using standard diabetic retinopathy datasets under controlled experimental conditions.

Results

DcareNet achieved a classification accuracy of 94.4% with an error rate of 5.58%, outperforming traditional approaches. These results highlight the mode’s reliability and potential for real-world clinical application, particularly in environments with limited ophthalmological resources.

Conclusion

DcareNet provides a scalable, cost-effective solution for diabetic retinopathy screening. It supports early intervention and bridges the gap between diagnostic demand and medical expertise in both developed and resource-constrained settings. The proposed approach aligns with the United Nations Sustainable Development Goals, specifically Goal 3 (Good Health and Well-being) and Goal 10 (Reduced Inequality), by promoting accessible, high-quality healthcare.