Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1783356875834654720 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6149821 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT xukele deepconvolutionalneuralnetworkbasedearlyautomateddetectionofdiabeticretinopathyusingfundusimage AT fengdawei deepconvolutionalneuralnetworkbasedearlyautomateddetectionofdiabeticretinopathyusingfundusimage AT mihaibo deepconvolutionalneuralnetworkbasedearlyautomateddetectionofdiabeticretinopathyusingfundusimage |