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

  • Gabriel García
  • Jhair Gallardo
  • Antoni Mauricio
  • Jorge LópezEmail author
  • Christian Del Carpio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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 



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.


  1. 1.
    Prentasic, R.P.: Detection of diabetic retinopathy in fundus photographs (2013)Google Scholar
  2. 2.
    Maher, R., Kayte, S., Dhopeshwarkar, D.M.: Review of automated detection for diabetes retinopathy using fundus images. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5(3) (2015)Google Scholar
  3. 3.
    Thomas, N., Mahesh, T.: Detecting clinical features of diabetic retinopathy using image processing. Int. J. Eng. Res. Technol. (IJERT) 3(8) (2014)Google Scholar
  4. 4.
    Singh, B., Jayasree, K.: Implementation of diabetic retinopathy detection system for enhance digital fundus images. Int. J. Adv. Technol. Innov. Res. 7(6), 0874–0876 (2015)Google Scholar
  5. 5.
    Gandhi, M., Dhanasekaran, R.: Diagnosis of diabetic retinopathy using morphological process and SVM classifier. In: 2013 International Conference on Communications and Signal Processing (ICCSP). IEEE (2013)Google Scholar
  6. 6.
    Sangwan, S., Sharma, V., Kakkar, M.: Identification of different stages of diabetic retinopathy. In: 2015 International Conference on Computer and Computational Sciences (ICCCS). IEEE (2015)Google Scholar
  7. 7.
    Shahin, E.M., et al.: Automated detection of diabetic retinopathy in blurred digital fundus images. In: 2012 8th International Computer Engineering Conference (ICENCO). IEEE (2012)Google Scholar
  8. 8.
    Karegowda, A.G., et al.: Exudates detection in retinal images using back propagation neural network. Int. J. Comput. Appli. 25(3), 25–31 (2011)Google Scholar
  9. 9.
    Kanth, S., Jaiswal, A., Kakkar, M.: Identification of different stages of diabetic retinopathy using artificial neural network. In: 2013 Sixth International Conference on Contemporary Computing (IC3). IEEE (2013)Google Scholar
  10. 10.
    Maji, D., et al.: Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2015)Google Scholar
  11. 11.
    Maji, D., et al. : Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images. arXiv preprint arXiv:1603.04833 (2016)
  12. 12.
    Pratt, H., et al.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200–205 (2016)CrossRefGoogle Scholar
  13. 13.
    Christine, N.: Your diabetic patients: look them in the eyes. Which ones will lose their sight? (2015).
  14. 14.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: 26th Advances In Neural Information Processing Systems (2012)Google Scholar
  15. 15.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICRL), 114 (2015)Google Scholar
  16. 16.
    Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gabriel García
    • 1
  • Jhair Gallardo
    • 1
  • Antoni Mauricio
    • 2
  • Jorge López
    • 2
    Email author
  • Christian Del Carpio
    • 1
  1. 1.Medical Image Processing Group, Department of Mechanical EngineeringUniversidad Nacional de IngenieríaLimaPeru
  2. 2.Department of Computer Science, Research Institute of Computer ScienceUniversidad Católica de San PabloArequipaPeru

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