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Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image

The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several differ...

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Detalles Bibliográficos
Autores principales: Xu, Kele, Feng, Dawei, Mi, Haibo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6149821/
https://www.ncbi.nlm.nih.gov/pubmed/29168750
http://dx.doi.org/10.3390/molecules22122054
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author Xu, Kele
Feng, Dawei
Mi, Haibo
author_facet Xu, Kele
Feng, Dawei
Mi, Haibo
author_sort Xu, Kele
collection PubMed
description The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several different feature extraction approaches have been proposed, the classification task for retinal images is still tedious even for those trained clinicians. Recently, deep convolutional neural networks have manifested superior performance in image classification compared to previous handcrafted feature-based image classification methods. Thus, in this paper, we explored the use of deep convolutional neural network methodology for the automatic classification of diabetic retinopathy using color fundus image, and obtained an accuracy of 94.5% on our dataset, outperforming the results obtained by using classical approaches.
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spelling pubmed-61498212018-11-13 Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image Xu, Kele Feng, Dawei Mi, Haibo Molecules Article The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several different feature extraction approaches have been proposed, the classification task for retinal images is still tedious even for those trained clinicians. Recently, deep convolutional neural networks have manifested superior performance in image classification compared to previous handcrafted feature-based image classification methods. Thus, in this paper, we explored the use of deep convolutional neural network methodology for the automatic classification of diabetic retinopathy using color fundus image, and obtained an accuracy of 94.5% on our dataset, outperforming the results obtained by using classical approaches. MDPI 2017-11-23 /pmc/articles/PMC6149821/ /pubmed/29168750 http://dx.doi.org/10.3390/molecules22122054 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Kele
Feng, Dawei
Mi, Haibo
Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image
title Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image
title_full Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image
title_fullStr Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image
title_full_unstemmed Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image
title_short Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image
title_sort deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6149821/
https://www.ncbi.nlm.nih.gov/pubmed/29168750
http://dx.doi.org/10.3390/molecules22122054
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