A new deep learning approach for the retinal hard exudates detection based on superpixel multi-feature extraction and patch-based CNN

Huang, Chenxi, Zong, Yongshuo, Ding, Yimin, Luo, Xin, Clawson, Kathy and Peng, Yonghong (2020) A new deep learning approach for the retinal hard exudates detection based on superpixel multi-feature extraction and patch-based CNN. Neurocomputing. ISSN 0925-2312 (In Press)

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Abstract

Diabetic Retinopathy (DR) is a severe complication of chronic diabetes which causes significant visual deterioration and, when coupled with delayed treatment, may lead to blindness. Exudative diabetic maculopathy, a form of macular edema where hard exudates (HE) develop, is a frequent cause of visual deterioration in DR. The detection of HE comprises a significant role in the DR diagnosis. In this paper, an automatic exudates detection method based on superpixel multi-feature extraction and patch-based deep convolutional neural network is proposed. Firstly, candidate superpixels are generated on each resized image using the superpixel segmentation algorithm called Simple Linear Iterative Clustering (SLIC). Then, 25 features extracted from resized images and patches are generated on each feature. Patches are subsequently used to train a deep convolutional neural network, which distinguishes hard exudates from the background. Experiments conducted on three publicly available datasets (DiaretDB1, e-ophtha EX and IDRiD) demonstrate that our proposed methodology achieved superior HE detection when compared with current state-of-art algorithms.

Item Type: Article
Divisions: Faculty of Technology > School of Computer Science
Depositing User: Kathy Clawson
Date Deposited: 24 Aug 2020 06:26
Last Modified: 24 Aug 2020 06:29
URI: http://sure.sunderland.ac.uk/id/eprint/12456
ORCID for Kathy Clawson: ORCID iD orcid.org/0000-0001-8431-1524
ORCID for Yonghong Peng: ORCID iD orcid.org/0000-0002-5508-1819

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