Document details

Exudate detection in fundus images using deeply-learnable features

Author(s): Khojasteh, Parham ; Passos Júnior, Leandro Aparecido ; Carvalho, Tiago ; Rezende, Edmar ; Aliahmad, Behzad ; Papa, João Paulo [UNESP] ; Kumar, Dinesh Kant

Date: 2019

Persistent ID:

Origin: Oasisbr

Subject(s): Convolutional neural networks; Deep learning; Deep residual networks; Diabetic retinopathy; Discriminative restricted Boltzmann machines; Exudate detection


Made available in DSpace on 2019-10-06T16:54:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-01

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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.

Royal Melbourne Institute of Technology Biosignals Laboratory School of Engineering, 124 La Trobe St

Federal University of São Carlos Department of Computing, Rod. Washington Luís, Km 235

Federal Institute of São Paulo Department of Computing

University of Campinas Institute of Computing

São Paulo State University - UNESP Department of Computing, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01

São Paulo State University - UNESP Department of Computing, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01

FAPESP: #2013/07375-0

FAPESP: #2014/12236-1

FAPESP: #2016/19403-6

FAPESP: #2016/50022-9

CNPq: #307066/2017-7

Document Type Journal article
Language English
Contributor(s) School of Engineering; Universidade Federal de São Carlos (UFSCar); Federal Institute of São Paulo; Universidade Estadual de Campinas (UNICAMP); Universidade Estadual Paulista (UNESP)
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