TY - JOUR
T1 - U-net based method for automatic hard exudates segmentation in fundus images using inception module and residual connection
AU - Zong, Yongshuo
AU - Chen, Jinling
AU - Yang, Lvqing
AU - Tao, Siyi
AU - Aoma, Cieryouzhen
AU - Zhao, Jiangsheng
AU - Wang, Shuihua
N1 - Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Deep learning
KW - Diabetic retinopathy
KW - Exudates segmentation
KW - Superpixel
KW - U-net
UR - http://www.scopus.com/inward/record.url?scp=85102875528&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3023273
DO - 10.1109/ACCESS.2020.3023273
M3 - Article
AN - SCOPUS:85102875528
SN - 2169-3536
VL - 8
SP - 167225
EP - 167235
JO - IEEE Access
JF - IEEE Access
ER -