Skip to main content
Springer Nature Link
Log in

Diabetic retinopathy detection and grading of retinal fundus images using coyote optimization algorithm with deep learning

  • Track 2: Medical Applications of Multimedia
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Diabetic retinopathy (DR) is a major reason of preventable blindness for diabetic patients. Regular retinal screening is recommended for diabetic persons to detect DR at the earlier stages. Manual retinal screening of DR is a difficult and laborious process, computer aided diagnosis models become essential. Recently deep learning (DL) methods enable effectual detection and classification of medical images, particularly retinal fundus images. With this motivation, this work presents an intelligent coyote optimization algorithm with DL based DR detection and grading (ICOA-DLDRD) model on retinal fundus images. The purpose of the ICOA-DLDRD approach is to identify the presence of DR on retinal fundus images. Primarily, the ICOA-DLDRD algorithm comprises Gabor filtering (GF) based noise removal and optimal region growing segmentation technique. Further, the primary seed points and thresholds of the region growing segmentation technique are optimally created utilizing the glowworm swarm optimization (GSO) algorithm. In addition, SqueezeNet with class attention learning (CAL) layer is derived for the extraction of feature vectors. Lastly, COA with a deep extreme learning machine (DELM) classifier is applied for the detection and grading of DR, in which the penalty parameter C and kernel parameter gamma γ of the DELM model are optimally adjusted by the use of COA. The performance validation of the ICOA-DLDRD method occurs utilizing the benchmark MESSIDOR dataset and the outcomes reported the betterment of the ICOA-DLDRD approach on the recent methods with maximum accuracy of 99.65%.

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
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Alyoubi WL, Abulkhair MF, Shalash WM (2021) Diabetic retinopathy fundus image classification and lesions localization system using deep learning. Sensors 21(11):3704

    Article  Google Scholar 

  2. Asiri N, Hussain M, Al Adel F, Alzaidi N (2019) Deep learning based computer-aided diagnosis systems for diabetic retinopathy: a survey. Artif Intell Med 99:101701

    Article  Google Scholar 

  3. Bhardwaj C, Jain S, Sood M (2021) Deep learning–based diabetic retinopathy severity grading system employing quadrant ensemble model. J Digit Imaging 34(2):440–457

    Article  Google Scholar 

  4. Chen K, Franko K and Sang R 2021 Structured model pruning of convolutional networks on tensor processing units. arXiv preprint arXiv:2107.04191

  5. Dai L, Wu L, Li H, Cai C, Wu Q, Kong H, Liu R, Wang X, Hou X, Liu Y, Long X (2021) A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat Commun 12(1):1–11

    Article  Google Scholar 

  6. Das S, Saha SK (2022) Diabetic retinopathy detection and classification using CNN tuned by genetic algorithm. Multimed Tools Appl 81(6):8007–8020

    Article  Google Scholar 

  7. Das S, Kharbanda K, Suchetha M, Raman R, Dhas E (2021) Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomed Signal Proc Contr 68:102600

    Article  Google Scholar 

  8. Ding S, Zhang N, Xu X, Guo L, Zhang J (2015) Deep extreme learning machine and its application in EEG classification. Math Probl Eng 2015:1–11

    MathSciNet  MATH  Google Scholar 

  9. Elsharkawy M, Sharafeldeen A, Soliman A, Khalifa F, Ghazal M, El-Daydamony E, Atwan A, Sandhu HS, El-Baz A (2022) A novel computer-aided diagnostic system for early detection of diabetic retinopathy using 3D-OCT higher-order spatial appearance model. Diagnostics 12(2):461

    Article  Google Scholar 

  10. Gangwar AK, Ravi V 2021 Diabetic retinopathy detection using transfer learning and deep learning. In evolution in computational intelligence (pp. 679-689). Springer, Singapore

  11. Hua Y, Mou L, Zhu XX (2019) Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification. ISPRS J Photogramm Remote Sens 149:188–199

    Article  Google Scholar 

  12. Imran M, Ullah A, Arif M, Noor R (2022) A unified technique for entropy enhancement based diabetic retinopathy detection using hybrid neural network. Comput Biol Med 145:105424

    Article  Google Scholar 

  13. Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Kälviäinen H, Pietilä J (2021) DIARETDB1 diabetic retinopathy database and evaluation protocol, Technical report (PDF)

  14. Kaushik H, Singh D, Kaur M, Alshazly H, Zaguia A, Hamam H (2021) Diabetic retinopathy diagnosis from fundus images using stacked generalization of deep models. IEEE Access 9:108276–108292

    Article  Google Scholar 

  15. Koonce B (2021) SqueezeNet. In convolutional neural networks with swift for Tensorflow (pp. 73-85). Apress, Berkeley, CA

  16. Li T, Gao Y, Wang K, Guo S, Liu H, Kang H (2019) Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Inf Sci 501:511–522

    Article  Google Scholar 

  17. Li F, Wang Y, Xu T, Dong L, Yan L, Jiang M, Zhang X, Jiang H, Wu Z and Zou H (2021) Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs. Eye, pp.1-9

  18. Majumder S, Kehtarnavaz N (2021) Multitasking deep learning model for detection of five stages of diabetic retinopathy. IEEE Access 9:123220–123230

    Article  Google Scholar 

  19. Pierezan J, Dos Santos Coelh, L Coyote (2018) Optimization Algorithm: A New Metaheuristic for Global Optimization Problems. In Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil; pp. 1–8

  20. Punitha S, Amuthan A, Joseph KS (2018) Benign and malignant breast cancer segmentation using optimized region growing technique. Future Comput Inform J 3(2):348–358

    Article  Google Scholar 

  21. Qummar S, Khan FG, Shah S, Khan A, Shamshirband S, Rehman ZU, Khan IA, Jadoon W (2019) A deep learning ensemble approach for diabetic retinopathy detection. IEEE Access 7:150530–150539

    Article  Google Scholar 

  22. Ryu G, Lee K, Park D, Park SH, Sagong M (2021) A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography. Sci Rep 11(1):1–9

    Article  Google Scholar 

  23. Saeed F, Hussain M, Aboalsamh HA (2021) Automatic diabetic retinopathy diagnosis using adaptive fine-tuned convolutional neural network. IEEE Access 9:41344–41359

    Article  Google Scholar 

  24. Saranya P, Prabakaran S, Kumar R, Das E (2021) Blood vessel segmentation in retinal fundus images for proliferative diabetic retinopathy screening using deep learning. Visual Comp 38:1–16

    Google Scholar 

  25. Shankar K, Zhang Y, Liu Y, Wu L, Chen CH (2020) Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access 8:118164–118173

    Article  Google Scholar 

  26. Shankar K, Sait ARW, Gupta D, Lakshmanaprabu SK, Khanna A, Pandey HM (2020) Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recogn Lett 133:210–216

    Article  Google Scholar 

  27. Song Z (2021) Facial expression emotion recognition model integrating philosophy and machine learning theory. Front Psychol 12

  28. Tang MCS, Teoh SS, Ibrahim H, Embong Z. (2022) A deep learning approach for the detection of neovascularization in fundus images using transfer learning. IEEE Access

  29. Vaishnavi J, Ravi S, Anbarasi A (2020) An efficient adaptive histogram based segmentation and extraction model for the classification of severities on diabetic retinopathy. Multimed Tools Appl 79(41):30439–30452

    Article  Google Scholar 

  30. Wang X, Yang K (2019) Economic load dispatch of renewable energy-based power systems with high penetration of large-scale hydropower station based on multi-agent glowworm swarm optimization. Ener Strategy Rev 26:100425

    Article  Google Scholar 

  31. Wu T, Liu L, Zhang T, Wu X (2022) Deep learning-based risk classification and auxiliary diagnosis of macular edema. Intelligence-Based Medicine, p 100053

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Annamalai University, Chidambaram, India

    K. Parthiban & M. Kamarasan

Authors
  1. K. Parthiban
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Kamarasan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Parthiban.

Ethics declarations

Data availability

Not applicable.

Competing interests and funding

The authors did not receive support from any organization for the submitted work.

Human participants and/or animals

Not applicable.

Conflict of interest

The authors have expressed no conflict of interest.

Additional information

Publisher’s note

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

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parthiban, K., Kamarasan, M. Diabetic retinopathy detection and grading of retinal fundus images using coyote optimization algorithm with deep learning. Multimed Tools Appl 82, 18947–18966 (2023). https://doi.org/10.1007/s11042-022-14234-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-14234-8

Keywords

Search

Navigation