Skip to main content

Advertisement

Springer Nature Link
Log in

RETRACTED ARTICLE: Detection of DR from retinal fundus images using prediction ANN classifier and RG based threshold segmentation for diabetes

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

This article was retracted on 28 November 2024

This article has been updated

Abstract

Diabetic retinopathy (DR) is one of the world’s most significant difficulties of diabetes identified with eye illness which happens when veins in the retina become swollen and releases liquid which at last prompts vision misfortune. Early discovery of DR can anticipate the harm to the retina and vision misfortune or atleast moderate its movement. There are various strategies used to distinguish DR in retinal fundus imageries which give inadmissible outcome for certain situations due to the morbidities of fundus images. In this paper, DR is analyzed by separating veins, optic plate and exudates from retinal fundus images by utilizing prediction ANN classifier and RG based segmentation approach. The performance of this segmentation approach is executed for different DR datasets and the outcomes demonstrates that the proposed segmentation approach produces 99% of accuracy in detection of DR which stands out from the various existing strategies.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Change history

References

  • Annie Grace Vimala GS, Kaja Mohideen S (2017) Diabetic retinopathy screening system: a validation analysis with multiple fundus image databases. Biomed Res 28(4):1598–1603

    Google Scholar 

  • Chen Y, Meng J, Li H, Wei H, Bi F, Liu S, Tang K, Guo H, Liu W (2019) Resveratrol exhibits an effect on attenuating retina inflammatory condition and damage of diabetic retinopathy via PON1. Exp Eye Res 181:356–366

    Article  Google Scholar 

  • David AM et al (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57:5200–5206

    Article  Google Scholar 

  • Godlin Atlas PK (2018) Detection of retinal hemorrhage from fundus images using ANFIS classifier and MRG segmentation. Biomed Res 29(7):1489–1497

    Google Scholar 

  • Kumaran Y, Chandrashekar PM (2018) Brief review of the detection of diabetic retinopathy in human eyes using pre-processing & segmentation techniques. Int J Recent Technol Eng 7(4):310–320

    Google Scholar 

  • Mirjalili S et al (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  • Mookiah MRK et al (2013) Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowl-Based Syst 39:9–22

    Article  Google Scholar 

  • Priya R, Aruna P (2011) Review of automated diagnosis of diabetic retinopathy using the support vector machine. Int J Appl Eng Res 1(4):844–863

    Google Scholar 

  • Varun G et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J Am Med Assoc 316(22):2402–2410

    Article  Google Scholar 

  • Vijila CKS, Anitha J (2009) Application of neuro-fuzzy model for MR brain tumor image classification. Int J Biomed Imaging 16:95–102

    Google Scholar 

  • Zekri Maryam HSM (2012) Review of medical image classification using the adaptive neuro-fuzzy inference system. J Med Signals Sensors 2(1):49–60

    Article  Google Scholar 

  • Zeynab M, Mahda N, Naghmeh M (2019) Automatic identification of diabetic retinopathy stages by using fundus images and visibility graph method. Measurement 140:133–141

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, St. Peter’s College of Engineering and Technology, Avadi, India

    P. Preethy Rebecca

  2. Department of CSE, Infant Jesus College of Engineering, Kilavallanadu, India

    S. Allwin

Authors
  1. P. Preethy Rebecca
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. S. Allwin
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to P. Preethy Rebecca.

Additional information

Publisher's Note

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

This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-024-04910-y

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Preethy Rebecca, P., Allwin, S. RETRACTED ARTICLE: Detection of DR from retinal fundus images using prediction ANN classifier and RG based threshold segmentation for diabetes. J Ambient Intell Human Comput 12, 10733–10740 (2021). https://doi.org/10.1007/s12652-020-02882-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02882-3

Keywords