Skip to main content
SpringerLink
Log in

A Survey on Automatic Diabetic Retinopathy Screening

  • Survey Article
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Diabetes is a chronic disease caused due to the increase in the sugar level in the blood. Diabetes mainly affects heart, blood vessels, kidneys, eyes and nerves. There are mellitus type 1 and 2 diabetes, in which insulin is either not taken by the body or not created by the body. As per the statistics provided by WHO, about 422 million people worldwide are fighting with diabetes. Primary source of vision loss in the patients suffering from diabetes is diabetic retinopathy where the individual suffers from the damage and growth of abnormal blood veins in the retina. The disease is observed by ophthalmologist through identifying the presence of abnormalities starting from microaneurysms in the non-proliferative stage of DR and if these lesions’ presence is ignored and not detected then it leads to the neovascularization in the proliferative stage which leads to unavoidable vision loss. DR can be cured if detected at the beginning stage. Manual method takes lots of time for detection of DR hence it is important to develop computer-based diagnostic system for DR detection using artificial intelligence (AI) and advance image processing to help ophthalmologists for spotting early symptoms of DR in less time. This paper provides a descriptive study about recent trends and technologies used for automatic spotting and grading of DR.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Laal M. Innovation process in medical imaging. Proced Soc Behav Sci. 2013;81:60–4.

    Google Scholar 

  2. Mellitus, D. I. A. B. E. T. E. S. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2006;29:S43.

    Google Scholar 

  3. Dean L, Jo ME. Introduction to diabetes. In: Ch M, editor. The genetic landscape of diabetes. Bethesda: National Center for Biotechnology Information (US); 2004.

    Google Scholar 

  4. International Diabetes Federation.IDF Diabetes Atlas, 9th edn. Brussels, Belgium. 2019. https://www.diabetesatlas.org.

  5. Pavate A, et al. Early prediction of five major complications ascends in diabetes mellitus using fuzzy logic. In: Soft computing in data analytics. Singapore: Springer; 2019. p. 759–68.

    Google Scholar 

  6. Lee R, Wong TY, Sabanayagam C. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vis. 2015;2:17. https://doi.org/10.1186/s40662-015-0026-2.

    Article  Google Scholar 

  7. Mateen M, et al. Automatic detection of diabetic retinopathy: a review on datasets, methods and evaluation metrics. IEEE Access. 2020;8:48784–811.

    Google Scholar 

  8. Litjens G, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.

    Google Scholar 

  9. Singh RP, editor. Managing diabetic eye disease in clinical practice. Berlin: Springer International Publishing; 2015.

    Google Scholar 

  10. Meriaudeau F, Sidibé D, Adal K, Ali S, Giancardo L, Karnowski TP, Chaum E. Computer Aided Design for Diabetic Retinopathy. QCAV2013. In: 11th International Conference on Quality Control by Artificial Vision, pp. 241–7.

  11. Chalakkala RJ, Waleed HA, Sheng CH. Fundus retinal image analyses for screening and diagnosing diabetic retinopathy, macular edema, and glaucoma disorders. In: El-Baz A, Suri JS, editors Computer-Assisted Diagnosis, Diabetes and Fundus. Elsevier. 2020;1 pp. 59–111. https://doi.org/10.1016/B978-0-12-817440-1.00003-6, ISBN 9780128174401.

  12. Mookiah MRK, et al. Computer-aided diagnosis of diabetic retinopathy: a review. Comput Biol Med. 2013;43(12):2136–55.

    Google Scholar 

  13. Lechner J, O’Leary OE, Stitt AW. The pathology associated with diabetic retinopathy. Vis Res. 2017;139:7–14.

    Google Scholar 

  14. Abràmoff MD, Garvin MK, Sonka M. Retinal imaging and image analysis. IEEE Rev Biomed Eng. 2010;3:169–208.

    Google Scholar 

  15. Lemaître G, et al. Classification of SD-OCT volumes using local binary patterns: experimental validation for DME detection. J Ophthalmol. 2016;2016:3298606. https://doi.org/10.1155/2016/3298606.

  16. Abdelsalam MM. Effective blood vessels reconstruction methodology for early detection and classification of diabetic retinopathy using OCTA images by artificial neural network. Inf Med Unlocked. 2020;20:100390.

    Google Scholar 

  17. Falavarjani KG, Irena T, Srinivas RS. Ultra-wide-field imaging in diabetic retinopathy. Vis Res. 2017;139:187–90.

    Google Scholar 

  18. Patel SN, et al. Ultra-widefield retinal imaging: an update on recent advances. Ther Adv Ophthalmol. 2020;12:251584149899495.

    Google Scholar 

  19. Stolte S, Ruogu F. A survey on medical image analysis in diabetic retinopathy. Med Image Anal. 2020;64:742.

    Google Scholar 

  20. Porwal P, et al. Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data. 2018;3(3):25.

    Google Scholar 

  21. Samiksha P, Prasanna P, Dhanshree T, Manesh K, Girish D, Vivek S, Luca G, Gwenolé Q, Fabrice M. Retinal fundus multi-disease image dataset (RFMiD). IEEE Dataport. 2020. https://doi.org/10.21227/s3g7-st65.

    Article  Google Scholar 

  22. Ganesan K, Martis RJ, Acharya UR, et al. Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images. Med Biol Eng Comput. 2014;52:663–72. https://doi.org/10.1007/s11517-014-1167-5.

    Article  Google Scholar 

  23. Akram MU, et al. RIDB: a Dataset of fundus images for retina based person identification. Data Brief. 2020;33:106433.

    Google Scholar 

  24. ElTanboly A, et al. A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images. Med Phys. 2017;44(3):914–23.

    Google Scholar 

  25. Goldbaum M, et al. Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images. In: Proceedings of 3rd IEEE international conference on image processing, IEEE, 1996;3:695–8.

  26. Aquino A, Manuel EG-A, Diego M. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Trans Med Imaging. 2010;29(11):1860–9.

    Google Scholar 

  27. Kamble R, et al. Localization of optic disc and fovea in retinal images using intensity based line scanning analysis. Comput Biol Med. 2017;87:382–96.

    Google Scholar 

  28. Zou B, et al. Classified optic disc localization algorithm based on verification model. Comput Graph. 2018;70:281–7.

    Google Scholar 

  29. Gui B, Shuai R-J, Chen P. Optic disc localization algorithm based on improved corner detection. Proced Comput Sci. 2018;131:311–9.

    Google Scholar 

  30. Dai B, Xiangqian Wu, Wei Bu. Optic disc segmentation based on variational model with multiple energies. Pattern Recogn. 2017;64:226–35.

    Google Scholar 

  31. Thakur N, Juneja M. Optic disc and optic cup segmentation from retinal images using hybrid approach. Expert Syst Appl. 2019;127:308–22.

    Google Scholar 

  32. Feijoo JG, de la Casa JMM, Servet HM, Zamorano MR, Mayoral MB, Suárez EJC. DRIONS-DB: digital retinal images for optic nerve segmentation database. 2009. http://www.ia.uned.es/∼ejcarmona/DRIONS-DB.html.

  33. Rehman ZU, et al. Multi-parametric optic disc segmentation using superpixel based feature classification. Expert Syst Appl. 2019;120:461–73.

    Google Scholar 

  34. Zhang L, Chee PL. Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models. Applied Soft Comput. 2020;92:106328.

    Google Scholar 

  35. Abdullah AS, et al. A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model. Med Biol Eng Comput. 2020;58(1):25–37.

    Google Scholar 

  36. Ramani RG, Jeslin JS. Improved image processing techniques for optic disc segmentation in retinal fundus images. Biomed Signal Process Control. 2020;58:832.

    Google Scholar 

  37. Kumar ES, Shoba CB. Two-stage framework for optic disc segmentation and estimation of cup-to-disc ratio using deep learning technique. J Ambient Intell Humaniz Comput. 2021;1–13.

  38. Krishna AV, et al. EffUnet-SpaGen: an efficient and spatial generative approach to glaucoma detection. J Imaging. 2021;7(6):92.

    Google Scholar 

  39. Hasan MK, et al. DRNet: segmentation and localization of optic disc and Fovea from diabetic retinopathy image. Artif Intell Med. 2021;111:102001.

    Google Scholar 

  40. Wang L, et al. Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network. Pattern Recogn. 2021;112:107810.

    Google Scholar 

  41. Pachade S, Prasanna P, Manesh K. A novel unsupervised framework for retinal vasculature segmentation. In: Advanced computational and communication paradigms. Singapore: Springer; 2018. p. 490–7.

    Google Scholar 

  42. Pachade S, Porwal P, Kokare M, Giancardo L, Meriaudeau F. Retinal vasculature segmentation and measurement framework for color fundus and SLO images. Biocybern Biomed Eng. 2020;40(3):865–900. https://doi.org/10.1016/j.bbe.2020.03.001 ISSN 0208-5216.

  43. Fan Z, Lu J, Rong Y. Automated blood vessel segmentation of fundus images using region features of vessels. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI). 2016, pp. 1–6. https://doi.org/10.1109/SSCI.2016.7849956.

  44. Jothi A, Shrinivas J. Blood vessel detection in fundus images using frangi filter technique. In: Smart innovations in communication and computational sciences. Singapore: Springer; 2019. p. 49–57.

    Google Scholar 

  45. Samuel PM, Thanikaiselvan V. VSSC Net: vessel specific skip chain convolutional network for blood vessel segmentation. Comput Methods Progr Biomed. 2020;198:105769.

    Google Scholar 

  46. Samuel PM, Thanikaiselvan V. VSSC Net: vessel specific skip chain convolutional network for blood vessel segmentation. Comput Methods Progr Biomed. 2021;198:769.

    Google Scholar 

  47. Atli İ, Osman SG. Sine-Net: a fully convolutional deep learning architecture for retinal blood vessel segmentation. Eng Sci Technol Int J. 2021;24(2):271–83.

    Google Scholar 

  48. Ramos-Soto O, et al. An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering. Comput Methods Progr Biomed. 2021;201:105949.

    Google Scholar 

  49. Dash S, et al. Illumination normalized based technique for retinal blood vessel segmentation. Int J Imaging Syst Technol. 2021;31(1):351–63.

    Google Scholar 

  50. Park K-B, Sung HC, Jae YL. M-gan: retinal blood vessel segmentation by balancing losses through stacked deep fully convolutional networks. IEEE Access. 2020;8:146308–22.

    Google Scholar 

  51. Tamim N, et al. Retinal blood vessel segmentation using hybrid features and multi-layer perceptron neural networks. Symmetry. 2020;12(6):894.

    Google Scholar 

  52. Mane VM, Ramish BK, Jadhav DV. Detection of Red lesions in diabetic retinopathy affected fundus images. In: 2015 IEEE International Advance Computing Conference (IACC), IEEE, 2015, pp. 56–60. https://doi.org/10.1109/IADCC.2015.7154668.

  53. Du J, et al. Automatic microaneurysm detection in fundus image based on local cross-section transformation and multi-feature fusion. Comput Methods Progr Biomed. 2020;196:687.

    Google Scholar 

  54. Manjaramkar A, Kokare M. Connected component clustering based hemorrhage detection in color fundus images. Int J Intell Eng Syst. 2018;11(2):143–51.

    Google Scholar 

  55. He Y, et al. Segmenting diabetic retinopathy lesions in multispectral images using low-dimensional spatial-spectral matrix representation. IEEE J Biomed Health Inf. 2019;24(2):493–502.

    Google Scholar 

  56. Khojasteh P, et al. Exudate detection in fundus images using deeply-learnable features. Comput Biol Med. 2019;104:62–9.

    Google Scholar 

  57. Munuera-Gifre E, et al. Analysis of the location of retinal lesions in central retinographies of patients with Type 2 diabetes. Acta Ophthalmol. 2020;98(1):e13–21.

    Google Scholar 

  58. Arrigo A, et al. Multicolor imaging to detect different subtypes of retinal microaneurysms in diabetic retinopathy. Eye. 2021;35(11):277–81.

    Google Scholar 

  59. Qiao L, Zhu Y, Zhou H. Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access. 2020;8:104292–302.

    Google Scholar 

  60. Shenavarmasouleh F, Arabnia HR. Drdr: automatic masking of exudates and microaneurysms caused by diabetic retinopathy using mask r-cnn and transfer learning. In Advances in Computer Vision and Computational Biology. Springer, Cham 2020, pp. 307–18

  61. Sambyal N, Saini P, Syal R, Gupta V. Modified U-Net architecture for semantic segmentation of diabetic retinopathy images. Biocybern Biomed Eng. 2020;40(3):1094–109.

    Google Scholar 

  62. Khojasteh P, et al. Introducing a novel layer in convolutional neural network for automatic identification of diabetic retinopathy. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. pp. 5938–41.

  63. Tan JH, et al. Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf Sci. 2017;420:66–76.

    Google Scholar 

  64. Al-Jarrah MA, Shatnawi H. Non-proliferative diabetic retinopathy symptoms detection and classification using neural network. J Med Eng Technol. 2017;41(6):498–505.

    Google Scholar 

  65. Shirbahadurkar SD, Mane VM, Jadhav DV. A modern screening approach for detection of diabetic retinopathy. In: 2017 2nd International Conference on Man and Machine Interfacing (MAMI). IEEE, 2017, pp. 1–6. https://doi.org/10.1109/MAMI.2017.8307893.

  66. Li X, et al. CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Trans Med Imaging. 2019;39(5):1483–93.

    Google Scholar 

  67. Hagos MT, Shri K, Surayya AB. Automated Smartphone Based System for Diagnosis of Diabetic Retinopathy. In: 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2019.

  68. Sisodia DS, Shruti N, Pooja K. Diabetic retinal fundus images: preprocessing and feature extraction for early detection of diabetic retinopathy. Biomed Pharmacol J. 2017;10(2):615–26.

    Google Scholar 

  69. Gangwar AK, Vadlamani R. Diabetic retinopathy detection using transfer learning and deep learning. In: Evolution in computational intelligence. Singapore: Springer; 2021. p. 679–89.

    Google Scholar 

  70. Mahmoud MH, et al. An automatic detection system of diabetic retinopathy using a hybrid inductive machine learning algorithm. Pers Ubiquitous Comput. 2021. pp. 1–15.

  71. Kamble VV, Kokate RD. Automated diabetic retinopathy detection using radial basis function. Proced Comput Sci. 2020;167:799–808.

    Google Scholar 

  72. Hacisoftaoglu RE, Karakaya M, Sallam AB. Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems. Pattern Recogn Lett. 2020;135:409–17.

    Google Scholar 

  73. Shankar K, et al. Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recogn Lett. 2020;133:210–6.

    Google Scholar 

  74. Wu Q, et al. Detection of morphologic patterns of diabetic macular edema using a deep learning approach based on optical coherence tomography images. Retina (Philadelphia Pa). 2021;41(5):1110.

    Google Scholar 

  75. Pranoto S, et al. Detection of diabetic macular edema in optical coherence tomography image using convolutional neural network. In: Proceedings of the 1st international conference on electronics, biomedical engineering, and health informatics. Singapore: Springer; 2021.

    Google Scholar 

  76. Thulkar D, Daruwala R, Sardar N. An integrated system for detection exudates and severity quantification for diabetic macular edema. J Med Biol Eng. 2020;40(6):798–820.

    Google Scholar 

  77. Wu J, et al. Diabetic macular edema grading based on improved faster R-CNN and MD-ResNet. Signal Image Video Process. 2020;15:743–51.

    Google Scholar 

  78. Odstrcilik J, et al. Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Process. 2013;7(4):373–83.

    MathSciNet  Google Scholar 

Download references

Funding

This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Usha Mittal Institute of Technology, SNDT Women’s University, UMIT, Mumbai, 400049, Maharashtra, India

    Pranoti Nage & Sanjay Shitole

Authors
  1. Pranoti Nage
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjay Shitole
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pranoti Nage.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Intelligent Computing and Networking” guest edited by Sangeeta Vhatkar, Seyedali Mirjalili, Jeril Kuriakose, P.D. Nemade, Arvind W. Kiwelekare, Ashok Sharma and Godson Dsilva.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nage, P., Shitole, S. A Survey on Automatic Diabetic Retinopathy Screening. SN COMPUT. SCI. 2, 439 (2021). https://doi.org/10.1007/s42979-021-00833-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00833-z

Keywords

Search

Navigation