Journal
INFORMATION SCIENCES
Volume 420, Issue -, Pages 66-76Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2017.08.050
Keywords
Exudates; Microaneurysms; Haemorrhages; Convolutional neural network; Fundus image; Segmentation; Diabetic retinopathy
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Screening for vision threatening diabetic retinopathy by grading digital retinal images reduces the risk of blindness in people with diabetes. Computer-aided diagnosis can aid human graders to cope with this mounting problem. We propose to use a 10-layer convolutional neural network to automatically, simultaneously segment and discriminate exudates, haemorrhages and micro-aneurysms. Input image is normalized before segmentation. The net is trained in two stages to improve performance. On average, our net on 30,275,903 effective points achieved a sensitivity of 0.8758 and 0.7158 for exudates and dark lesions on the CLEOPATRA database. It also achieved a sensitivity of 0.6257 and 0.4606 for haemorrhages and micro-aneurysms. This study shows that it is possible to get a single convolutional neural network to segment these pathological features on a wide range of fundus images with reasonable accuracy. (C) 2017 Elsevier Inc. All rights reserved.
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