Skip to main content

Advertisement

SpringerLink
Log in

Convolutional neural network-based sea lion optimization algorithm for the detection and classification of diabetic retinopathy

  • Original Article
  • Published:
Acta Diabetologica Aims and scope Submit manuscript

Abstract

Aims

Diabetic retinopathy (DR) becomes a complicated type of diabetic that causes damage to the blood vessels of the retina’s light-sensitive tissue. DR may initially cause mild symptoms or no symptoms. But prolonged DR results in permanent vision loss, and hence, it is necessary to detect the DR at an early stage.

Methods

Manual diagnosing of DR retina fundus image is a time-consuming process and sometimes leads to misdiagnosis. The existing DR detection model faces few shortcomings in case of improper detection accuracy, higher loss or error values, high feature dimensionality, not suitable for large datasets, high computational complexity, poor performances, unbalanced and limited number of data points, and so on. As a result, the DR is diagnosed in this paper through four critical phases to tackle the shortcomings. The retinal images are cropped during preprocessing to reduce unwanted noises and redundant data. The images are then segmented using a modified level set algorithm based on pixel characteristics.

Results

Here, an Aquila optimizer is employed in extracting the segmented image. Finally, for optimal classification of DR images, the study proposes a convolutional neural network-oriented sea lion optimization (CNN-SLO) algorithm. Here, the CNN-SLO algorithm classifies the retinal images into five classes (healthy, moderate, mild, proliferative and severe).

Conclusion

The experimental investigation is performed for Kaggle datasets with respect to diverse evaluation measures to deliberate the performances of the proposed system.

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
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Availability of data and material

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Code availability

Not applicable.

References

  1. Faura G, Boix-Lemonche G, Holmeide AK et al (2022) Colorimetric and electrochemical screening for early detection of diabetes mellitus and diabetic retinopathy—application of sensor arrays and machine learning. Sensors 22(3):718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gupta S, Thakur S, Gupta A (2022) Optimized hybrid machine learning approach for smartphone based diabetic retinopathy detection. Multimed Tools Appl 81(10):14475–14501

    Article  PubMed  PubMed Central  Google Scholar 

  3. 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 

  4. Lim WX, Chen Z, Ahmed A (2022) The adoption of deep learning interpretability techniques on diabetic retinopathy analysis: a review. Med Biol Eng Comput, pp1–10

  5. Masud M, Alhamid MF, Zhang Y (2022) A convolutional neural network model using weighted loss function to detect diabetic retinopathy. ACM Trans Multimed Comput Commun Appl (TOMM) 18(1):1–16

    Google Scholar 

  6. Elsharkawy M, Sharafeldeen A, Soliman A et al (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  PubMed  PubMed Central  Google Scholar 

  7. Luo X, Pu Z, Xu Y et al (2021) MVDRNet: Multi-view diabetic retinopathy detection by combining DCNNs and attention mechanisms. Pattern Recogn 120:108104

    Article  Google Scholar 

  8. Kalyani G, Janakiramaiah B, Karuna A, Prasad LV (2021) Diabetic retinopathy detection and classification using capsule networks. Complex Intell Syst, pp 1–14

  9. Qureshi I, Ma J, Abbas Q (2021) Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning. Multimed Tools Appl 80(8):11691–11721

    Article  Google Scholar 

  10. Sungheetha A, Sharma R (2021) Design an early detection and classification for diabetic retinopathy by deep feature extraction based convolution neural network. J Trends Comput Sci Smart Technol (TCSST) 3(02):81–94

    Article  Google Scholar 

  11. Bhardwaj C, Jain S, Sood M (2021) Transfer learning-based robust automatic detection system for diabetic retinopathy grading. Neural Comput Appl 33(20):13999–14140

    Article  Google Scholar 

  12. Jadhav AS, Patil PB, Biradar S (2021) Optimal feature selection-based diabetinopathy detection using improved rider optimization algorithm enabled with deep learning

  13. Gadekallu TR, Khare N, Bhattacharya S, Singh S, Maddikunta PKR, Srivastava G (2020) Deep neural networks to predict diabetic retinopathy. J Ambient Intell Humaniz Comput, pp 1–14

  14. Karthikeyan R, Alli P (2018) Feature selection and parameters optimization of support vector machines based on hybrid glowworm swarm optimization for classification of diabetic retinopathy. J Med Syst 42(10):1–11

    Article  Google Scholar 

  15. Taufiqurrahman S, Handayani A, Hermanto BR et al. (2020) Diabetic retinopathy classification using a hybrid and efficient MobileNetV2-SVM model. In 2020 IEEE Region 10 Conference (Tencon) (pp. 235–240). IEEE.Conference (TENCON) (pp 235–240). IEEE

  16. Shankar K, Perumal E, Vidhyavathi RM (2020) Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images. SN Appl Sci 2(4):1–10

    Article  Google Scholar 

  17. Bodapati JD, Shaik NS, Naralasetti V (2021) Composite deep neural network with the gated-attention mechanism for diabetic retinopathy severity classification. J Ambient Intell Humaniz Comput 12(10):9825–9839

    Article  Google Scholar 

  18. Math L, Fatima R (2021) Adaptive machine learning classification for diabetic retinopathy. Multimedia Tools Appl 80(4):5173–5186

    Article  Google Scholar 

  19. Das S, Kharbanda K, Suchetha M et al (2021) Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomed Signal Process Control 68:102600

    Article  Google Scholar 

  20. Sharma A, Shinde S, Shaikh II et al. (2021) Machine learning approach for detection of diabetic retinopathy with improved pre-processing. In 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp 517–522). IEEE.

  21. Cai LY, Yang Q, Hansen CB et al (2021) PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images. Magn Reson Med 86(1):456–470

    Article  PubMed  PubMed Central  Google Scholar 

  22. Amarapur B (2019) Cognition-based MRI brain tumor segmentation technique using modified level set method. Cogn Technol Work 21(3):357–369

    Article  Google Scholar 

  23. Abualigah L, Yousri D, Abd Elaziz M et al (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250

    Article  Google Scholar 

  24. Desai M, Shah M (2021) An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth 4:1–11

    Article  Google Scholar 

  25. Masadeh R, Alsharman N, Sharieh A et al (2021) Task scheduling on cloud computing based on sea lion optimization algorithm. Int J Web Inf Syst 17(2):99–116

    Article  Google Scholar 

  26. https://www.kaggle.com/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data?select=colored_images

  27. Iglicki M, Busch C, Zur D et al (2019) Dexamethasone implant for diabetic macular edema in naive compared with refractory eyes: the international retina group real-life 24-month multicenter study. IRGREL-DEX Study Retina 39(1):44–51

    Article  CAS  PubMed  Google Scholar 

  28. Iglicki M, Zur D, Busch C et al (2018) Progression of diabetic retinopathy severity after treatment with dexamethasone implant: a 24-month cohort study the ‘DR-Pro-DEX Study.’ Acta Diabetol 55:541–547

    Article  CAS  PubMed  Google Scholar 

  29. Iglicki M, Zur D, Fung A et al (2019) TRActional DIabetic reTInal detachment surgery with co-adjuvant intravitreal dexamethasONe implant: the TRADITION STUDY. Acta Diabetol 56:1141–1147

    Article  CAS  PubMed  Google Scholar 

  30. Iglicki M, Zur D, Negri HP et al (2020) Results in comparison between 30 gauge ultrathin wall and 27 gauge needle in sutureless intraocular lens flanged technique in diabetic patients: 24-month follow-up study. Acta Diabetol 57:1151–1157

    Article  PubMed  Google Scholar 

  31. Tang F, Luenam P, Ran AR et al (2021) Detection of diabetic retinopathy from ultra-widefield scanning laser ophthalmoscope images: a multicenter deep learning analysis. Ophthalmology Retina 5(11):1097–1106

    Article  PubMed  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education (Deemed to be University), Srivilliputhur, Tamil Nadu, India

    S. V. Hemanth, Saravanan Alagarsamy & T. Dhiliphan Rajkumar

Authors
  1. S. V. Hemanth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saravanan Alagarsamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. Dhiliphan Rajkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SVH agreed on the content of the study. SVH and SA collected all the data for analysis. SVH agreed on the methodology. SVH and SA completed the analysis based on agreed steps. Results and conclusions are discussed and written together. Both author read and approved the final manuscript.

Corresponding author

Correspondence to S. V. Hemanth.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed Consent

Informed consent does not apply as this was a retrospective review with no identifying patient information.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

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

This article belongs to the topical collection Eye Complications of Diabetes, managed by Giuseppe Querques.

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

Hemanth, S.V., Alagarsamy, S. & Dhiliphan Rajkumar, T. Convolutional neural network-based sea lion optimization algorithm for the detection and classification of diabetic retinopathy. Acta Diabetol 60, 1377–1389 (2023). https://doi.org/10.1007/s00592-023-02122-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00592-023-02122-y

Keywords

Search

Navigation