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Diabetes is a very common disease worldwide. It is primary cause of blindness for people having age less than 50 years. Diabetic retinopathy is a common retinal complication associated with diabetes. Screening and classifying malignancies in diabetic retinopathy is a challenging task because it is symptomatic. Several techniques are available for the identification of abnormality. In this paper algorithms based on deep learning and convolution neural networks are reviewed for diagnosis of Diabetic Retinopathy. A comparison of all methods based on preprocessing techniques, types of feature extraction, convolution neural network architectures and performance metrics is presented. It is observed that Accuracy of any technique depends upon various factors like number of preprocessing steps, number of features used, and number of convolution layers, number and type of data sets used for training the model at the cost of complexity.


Diabetic Retinopathy; Deep Learning; Convolution Neural Network; Microneurysms; Hemorrhages; Exudates.

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How to Cite
Shital N. Firke and Ranjan Bala Jain, “RECENT ADVANCES IN AUTOMATED DETECTION AND CLASSIFICATION OF DIABETIC RETINOPATHY USING DEEP LEARNING”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 5, p. 11, Jun. 2020.


  1. Zahira Asifa et al., “Detection of Diabetic Retinopathy with Feature Extraction using Image Processing”, International Journal of Electrical, Electronics and Computer Systems, pp. 1-4, 2015.
  2. Alexander Rakhlin et al., “Diabetic Retinopathy detection through integration of Deep Learning classification framework”, pp 1-11, February 2017.
  3. Ankita Gupta et al., “Diabetic Retinopathy: Present and Past” International Conference on Computational Intelligence and Data Science, pp. 1432-1440, 2018.
  4. Wafa Aladawi et al., “Recent Innovations in Automated Detection and Classification of Diabetic Retinopathy”, International Journal of Innovative Technology and Exploring Engineering, pp-1997-2004, 2019.
  5. Keiron Shea et al., “An Introduction to Convolutional Neural Networks”, pp. 1-11, 2015.
  6. Christian Szegedy et al., “Going deeper with convolutions”, pp. 1-12, 2014.
  7. Krizhevsky et al., “ImageNet classification with deep convolutional neural networks”, pp. 1097-1105, 2012.
  8. Aston Zhang et al., “Drive into Deep Learning” 2020.
  9. Karen Simonyan et al., “Very Deep Convolutional Networks for Large- Scale Image Recognition”, pp. 1-14, 2015.
  10. Pavle Prentasi et al., “Detection of Exudates in Fundus Photographs using Convolutional Neural Networks”, 9th International Symposium on Image and Signal Processing and Analysis, pp. 188-192, IEEE, 2015.
  11. Darshit Doshi et al., “Diabetic retinopathy using deep convolution neural network”, International Conference on Computing, Analytics and Security Trends IEEE, pp. 261-266, 2016.
  12. Harry Pratta et al., “Convolutional Neural Networks for Diabetic Retinopathy”, International Conference on Medical Imaging Under- standing and Analysis, pp. 200-205, 2016.
  13. [13]G. Lin et al., “Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy”, Journal of Ophthalmology, pp.1-6, 2018.
  14. Sairaj Burewar et al., “Diabetic Retinopathy Detection by Retinal segmentation with Region merging using CNN”, 13th International Conference on Industrial and Information Systems IEEE, pp. 136-142, 2018.
  15. Chandrkumar T and R Kathirvel, “Classifying Diabetic Retinopathy using Deep Learning Architecture”, International Journal of Engineering Research, pp. 19-24, 2016.
  16. Kele Xu et al., “Deep Convolutional Neural Network Based Early Automated Detection of Diabetic Retinopathy using Fundus Image”, Molecules, vol.22, no.12, pp.1-7, 2017.
  17. Jaun Shan et al., “A Deep Learning Method for Microneurysms Detection in Fundus Image”, First International Conference on Connected Health: Applications, Systems and Engineering Technologies, pp. 357- 358, IEEE, 2016.
  18. S. N. Sangeethaa and P. Uma Maheswari “An Intelligent Model for Blood Vessel Segmentation in
  19. Diagnosing DR Using CNN”, Journal of Medical Systems Springer, pp. 1-10 August 2018.
  20. Maya K V and Adarsh K S “Detection of Retinal Lesions Based on Deep Learning for Diabetic Retinopathy”, Fifth International Conference on Electrical Energy Systems IEEE, 2019.
  21. M. Hazim Johari et al., “Early Detection of Diabetic Retinopathy by using Deep Learning Neural Network”, International Journal of Engineering and Technology, pp. 198-201, 2018.
  22. Carson Lam et al., “Automated Detection of Diabetic Retinopathy using Deep Learning” , pp. 147-155, 2019.
  23. Standard Diabetic Retinopathy Database Calibration level 1. Available online at “”. Accessed on: 02-03-2020.
  24. Diabetic Retinopathy Detection Kaggle Data-sets. Available online at “ retinopathy-detection/data”. Accessed on: 02-03-2020.
  25. Diabetic Retinopathy Detection Drive Datasets. Available online at “”. Accessed on: 02-03-2020.
  26. Diabetic Retinopathy Detection STARE Datasets. Available online at “ ahoover/stare”. Accessed on: 02-03-2020. [26]Diabetic Retinopathy Detection MESSIDOR Datasets. Available online
  27. at “”. Accessed on: 02-03- 2020.
  28. [27]Diabetic Retinopathy Detection EyePACS Datasets. Available online at “ breakthrough-using-eyepacs-retinal-images”. Accessed on: 02-03-2020.