Scholarlink

Diabetic retinopathy (DR) is a diabetes-related ocular complication that can lead to vision loss. Its detection is performed through fundus examinations, assisted by lesion segmentation techniques. The IDRiD and APTOS-2019 datasets are used for DR lesion segmentation and classification, respectively. Using the U-Net architecture, lesions such as microaneurysms and exudates were segmented, while CNNs classified disease stages. In this paper, we present DR-AIVis, an approach for segmentation, classification, and explainability of diabetic retinopathy (DR). Our results demonstrate an accuracy of 93.84% in segmentation and 98.30% in classification. Additionally, we employ Grad-CAM to highlight the most relevant regions of the image. As contributions, our work includes an automated system for DR segmentation and classification, as well as a mechanism to identify the most important image regions for decision-making, thereby enhancing confidence in the provided results using Grad-CAM.

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

DR-AIVis: A Hybrid Approach for Diabetic Retinopathy Detection Using U-Net Segmentation and CNN Classification with Grad-CAM Explainability

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Marcelo Colares da Silva,
  • Caio Marques Silva,
  • Suane Pires P. da Silva,
  • Roger M. Sarmento,
  • Houbing Hebert Song,
  • Pedro Pedrosa Rebouças Filho

摘要

Diabetic retinopathy (DR) is a diabetes-related ocular complication that can lead to vision loss. Its detection is performed through fundus examinations, assisted by lesion segmentation techniques. The IDRiD and APTOS-2019 datasets are used for DR lesion segmentation and classification, respectively. Using the U-Net architecture, lesions such as microaneurysms and exudates were segmented, while CNNs classified disease stages. In this paper, we present DR-AIVis, an approach for segmentation, classification, and explainability of diabetic retinopathy (DR). Our results demonstrate an accuracy of 93.84% in segmentation and 98.30% in classification. Additionally, we employ Grad-CAM to highlight the most relevant regions of the image. As contributions, our work includes an automated system for DR segmentation and classification, as well as a mechanism to identify the most important image regions for decision-making, thereby enhancing confidence in the provided results using Grad-CAM.