Exudate detection in fundus images using deeply-learnable features

Khojasteh, P, Junior, L, Carvalho, T, Rezende, E, Aliahmad, B, Papa, J and Kumar, D 2019, 'Exudate detection in fundus images using deeply-learnable features', Computers in Biology and Medicine, vol. 104, pp. 62-69.


Document type: Journal Article
Collection: Journal Articles

Title Exudate detection in fundus images using deeply-learnable features
Author(s) Khojasteh, P
Junior, L
Carvalho, T
Rezende, E
Aliahmad, B
Papa, J
Kumar, D
Year 2019
Journal name Computers in Biology and Medicine
Volume number 104
Start page 62
End page 69
Total pages 8
Publisher Elsevier
Abstract Presence of exudates on a retina is an early sign of diabetic retinopathy, and automatic detection of these can improve the diagnosis of the disease. Convolutional Neural Networks (CNNs) have been used for automatic exudate detection, but with poor performance. This study has investigated different deep learning techniques to maximize the sensitivity and specificity. We have compared multiple deep learning methods, and both supervised and unsupervised classifiers for improving the performance of automatic exudate detection, i.e., CNNs, pre-trained Residual Networks (ResNet-50) and Discriminative Restricted Boltzmann Machines. The experiments were conducted on two publicly available databases: (i) DIARETDB1 and (ii) e-Ophtha. The results show that ResNet-50 with Support Vector Machines outperformed other networks with an accuracy and sensitivity of 98% and 0.99, respectively. This shows that ResNet-50 can be used for the analysis of the fundus images to detect exudates.
Subject Biomedical Engineering not elsewhere classified
Signal Processing
Keyword(s) Convolutional neural networks
Deep learning
Deep residual networks
Diabetic retinopathy
Discriminative restricted Boltzmann machines
Exudate detection
DOI - identifier 10.1016/j.compbiomed.2018.10.031
Copyright notice © 2018 Elsevier Ltd. All rights reserved.
ISSN 0010-4825
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