Journal
IET COMPUTER VISION
Volume 14, Issue 1, Pages 1-8Publisher
WILEY
DOI: 10.1049/iet-cvi.2018.5508
Keywords
feature extraction; biomedical optical imaging; image segmentation; medical image processing; object detection; eye; diseases; image classification; blood vessels; feature pyramid network; modified region proposal network; DR-grading model; Messidor data; Shanghai Eye Hospital; hospital data; modified R-FCN lesion-detection model; lesion detection; modified R-FCN object-detection algorithm; computer-aided retinal image screening system; retinal fundus images; modified object-detection method; region-based fully convolutional network
Funding
- Scientific and Technological Innovation Action Plan of the Science and Technology Commission of Shanghai Municipality [17511107900]
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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.
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