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Molecules 2017, 22(12), 2054; doi:10.3390/molecules22122054

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

1
School of Information and Communication, National University of Defense Technology, Wuhan 430019, China
2
School of Computer, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
Received: 10 November 2017 / Revised: 20 November 2017 / Accepted: 22 November 2017 / Published: 23 November 2017
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Abstract

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. View Full-Text
Keywords: diabetic retinopathy; deep convolutional neural network; image classification diabetic retinopathy; deep convolutional neural network; image classification
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Xu, K.; Feng, D.; Mi, H. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image. Molecules 2017, 22, 2054.

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