Open AccessAutomated diabetic retinopathy grading and lesion detection based on the modified R‐FCN object‐detection algorithm
Author(s) -
Wang Jialiang,
Luo Jianxu,
Liu Bin,
Feng Rui,
Lu Lina,
Zou Haidong
Publication year - 2020
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5508
Subject(s) - artificial intelligence , computer science , diabetic retinopathy , pattern recognition (psychology) , grading (engineering) , object detection , computer vision , convolutional neural network , fundus (uterus) , data set , medicine , radiology , civil engineering , engineering , diabetes mellitus , endocrinology
In this work, we develop a computer‐aided retinal image screening system that can perform automated diabetic retinopathy (DR) grading and DR lesion detection in retinal fundus images. We propose a modified object‐detection method for this task via a region‐based fully convolutional network (R‐FCN). A feature pyramid network and a modified region proposal network are applied to enhance the detection of small objects. The DR‐grading model based on the modified R‐FCN is evaluated on the Messidor data set and images provided by the Shanghai Eye Hospital. High sensitivity of 99.39% and specificity of 99.93% are obtained on the hospital data. Moreover, high sensitivity of 92.59% and specificity of 96.20% are obtained on the Messidor data set. The modified R‐FCN lesion‐detection model is validated on the hospital data set and achieves a 92.15% mean average precision. The proposed R‐FCN can efficiently accomplish DR grading and lesion detection with high accuracy.