| Texto completo | |
| Autor(es): |
Khojasteh, Parham
[1]
;
Passos Junior, Leandro Aparecido
[2]
;
Carvalho, Tiago
[3]
;
Rezende, Edmar
[4]
;
Aliahmad, Behzad
[1]
;
Papa, Joao Paulo
[5]
;
Kumar, Dinesh Kant
[1]
Número total de Autores: 7
|
| Afiliação do(s) autor(es): | [1] RMIT Univ, Sch Engn, Biosignals Lab, 124 La Trobe St, Melbourne, Vic - Australia
[2] Univ Fed Sao Carlos, Dept Comp, Rod Washington Luis, Km 235, BR-13565905 Sao Carlos, SP - Brazil
[3] Fed Inst Sao Paulo, Dept Comp, BR-13069901 Campinas, SP - Brazil
[4] Univ Estadual Campinas, Inst Comp, BR-13069901 Campinas, SP - Brazil
[5] Sao Paulo State Univ UNESP, Dept Comp, Av Eng Luiz Edmund Carrijo Coube 14-01, BR-17033360 Bauru - Brazil
Número total de Afiliações: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | COMPUTERS IN BIOLOGY AND MEDICINE; v. 104, p. 62-69, JAN 2019. |
| Citações Web of Science: | 6 |
| Resumo | |
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. (AU) | |
| Processo FAPESP: | 16/50022-9 - Development of cardiovascular disease and diabetes risk assessment model for ethnically diverse populations |
| Beneficiário: | João Paulo Papa |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria |
| Beneficiário: | Francisco Louzada Neto |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |
| Processo FAPESP: | 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo? |
| Beneficiário: | Alexandre Xavier Falcão |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações |
| Beneficiário: | João Paulo Papa |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |