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Detection of Diabetic Retinopathy Based on a Convolutional Neural Network Using Retinal Fundus Images

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10614)


Diabetic retinopathy is one of the leading causes of blindness. Its damage is associated with the deterioration of blood vessels in retina. Progression of visual impairment may be cushioned or prevented if detected early, but diabetic retinopathy does not present symptoms prior to progressive loss of vision, and its late detection results in irreversible damages. Manual diagnosis is performed on retinal fundus images and requires experienced clinicians to detect and quantify the importance of several small details which makes this an exhaustive and time-consuming task. In this work, we attempt to develop a computer-assisted tool to classify medical images of the retina in order to diagnose diabetic retinopathy quickly and accurately. A neural network, with CNN architecture, identifies exudates, micro-aneurysms and hemorrhages in the retina image, by training with labeled samples provided by EyePACS, a free platform for retinopathy detection. The database consists of 35126 high-resolution retinal images taken under a variety of conditions. After training, the network shows a specificity of 93.65% and an accuracy of 83.68% on validation process.


  • Diabetic retinopathy
  • Deep learning
  • Convolutional neural network
  • Medical image classification

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  • DOI: 10.1007/978-3-319-68612-7_72
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The present work would not be possible without the funds of the General Research Institute (IGI - UNI), The Office of Research (VRI - UNI), The Research Institute of Computer Science (RICS - UCSP) and the support of the Artificial Intelligence and Robotics Lab.

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Correspondence to Jorge López .

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García, G., Gallardo, J., Mauricio, A., López, J., Del Carpio, C. (2017). Detection of Diabetic Retinopathy Based on a Convolutional Neural Network Using Retinal Fundus Images. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham.

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