U-net based method for automatic hard exudates segmentation in fundus images using inception module and residual connection

Yongshuo Zong, Jinling Chen, Lvqing Yang*, Siyi Tao, Cieryouzhen Aoma, Jiangsheng Zhao, Shuihua Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

Diabetic retinopathy (DR) is an eye abnormality caused by chronic diabetes that affected patients worldwide. Hard exudate is an important and observable sign of DR and can be used for early diagnosis. In this paper, an automatic hard exudates segmentation method is proposed in order to aid ophthalmologists to diagnose DR in the early stage. We utilized the SLIC superpixel algorithm to generate sample patches, thus overcoming the difficulty of the limited and imbalanced dataset. Furthermore, a U-net based network architecture with inception modules and residual connections is proposed to conduct end-to-end hard exudate segmentation, and focal loss is utilized as the loss function. Extensive experiments have been conducted on the IDRiD dataset to evaluate the performance of the proposed method. The reported sensitivity, specificity, and accuracy achieve 96.38%, 97.14%, and 97.95% respectively, which demonstrates the effectiveness and superiority of our method. The achieved segmentation results prove the potential of the method for clinical diagnosis.

Original languageEnglish
Pages (from-to)167225-167235
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Deep learning
  • Diabetic retinopathy
  • Exudates segmentation
  • Superpixel
  • U-net

Fingerprint

Dive into the research topics of 'U-net based method for automatic hard exudates segmentation in fundus images using inception module and residual connection'. Together they form a unique fingerprint.

Cite this