Automated Segmentation of Exudates, Haemorrhages, Microaneurysms using Single Convolutional Neural Network

Bhandary, Sulatha V (2017) Automated Segmentation of Exudates, Haemorrhages, Microaneurysms using Single Convolutional Neural Network. Information Sciences, 420. pp. 66-76. ISSN 0020-0255

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Exudates; Microaneurysms; Haemorrhages; Convolutional neural network; Fundus image; Segmentation; Diabetic retinopathy
Subjects: Medicine > KMC Manipal > Ophthalmology
Depositing User: KMC Library
Date Deposited: 21 Feb 2018 04:41
Last Modified: 21 Feb 2018 04:41
URI: http://eprints.manipal.edu/id/eprint/150621

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