Skip to main content

Advertisement

SpringerLink
Log in

A novel four-step feature selection technique for diabetic retinopathy grading

  • Scientific Paper
  • Published:
Physical and Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

Diabetic retinopathy is a microvascular complication of diabetes mellitus that develops over time. Diabetic retinopathy is one of the retinal disorders. Early detection of diabetic retinopathy reduces the chances of permanent vision loss. However, the identification and regular diagnosis of diabetic retinopathy is a time-consuming task and requires expert ophthalmologists and radiologists. In addition, an automatic diabetic retinopathy detection technique is necessary for real-time applications to facilitate and minimize potential human errors. Therefore, we propose an ensemble deep neural network and a novel four-step feature selection technique in this paper. In the first step, the preprocessed entropy images improve the quality of the retinal features. Second, the features are extracted using a deep ensemble model include InceptionV3, ResNet101, and Vgg19 from the retinal fundus images. Then, these features are combined to create an ample feature space. To reduce the feature space, we propose four-step feature selection techniques: minimum redundancy, maximum relevance, Chi-Square, ReliefF, and F test for selecting efficient features. Further, appropriate features are chosen from the majority voting techniques to reduce the computational complexity. Finally, the standard machine learning classifier, support vector machines, is used in diabetic retinopathy classification. The proposed method is tested on Kaggle, MESSIDOR-2, and IDRiD databases, available publicly. The proposed algorithm provided an accuracy of 97.78%, a sensitivity of 97.6%, and a specificity of 99.3%, using top 300 features, which are better than other state-of-the-art methods.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Devi J, Nagur B, Shaik S, Naralasetti V (2021) Composite deep neural network with gated—attention mechanism for diabetic retinopathy severity classification. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02727-z

    Article  Google Scholar 

  2. Sikder N, Masud M, Bairagi AK, Arif ASM, Nahid A-A, Alhumyani HA (2021) Severity classification of diabetic retinopathy using an ensemble learning algorithm through analyzing retinal images. Symmetry (Basel) 13(4):670. https://doi.org/10.3390/sym13040670

    Article  Google Scholar 

  3. Jagan Mohan N, Murugan R, Goel T, Roy P (2020) An improved accuracy rate in microaneurysms detection in retinal fundus images using non-local mean filter. Commun Comput Inf Sci 1240:183–193. https://doi.org/10.1007/978-981-15-6315-7_15

    Article  Google Scholar 

  4. Jagan Mohan N, Murugan R, Goel T, Roy P (2021) Exudate localization in retinal fundus images using modified speeded up robust features algorithm. 2020 IEEE-EMBS conference on biomedical engineering and sciences (IECBES). IEEE, Langkawi Island, pp 367–371. https://doi.org/10.1109/IECBES48179.2021.9398771

    Chapter  Google Scholar 

  5. Jagan Mohan N, Murugan R, Goel T, Roy P (2020) Optic disc segmentation in fundus images using operator splitting approach. 2020 advanced communication technologies and signal processing (ACTS). IEEE, Silchar. https://doi.org/10.1109/ACTS49415.2020.9350504

    Chapter  Google Scholar 

  6. Bhardwaj C, Jain S, Sood M (2021) Hierarchical severity grade classification of non-proliferative diabetic retinopathy. J Ambient Intell Humaniz Comput 12(2):2649–2670. https://doi.org/10.1007/s12652-020-02426-9

    Article  Google Scholar 

  7. Alyoubi WL, Shalash WM, Abulkhair MF (2020) Diabetic retinopathy detection through deep learning techniques: a review. Inform Med Unlocked. https://doi.org/10.1016/j.imu.2020.100377

    Article  Google Scholar 

  8. Mansour RF (2018) Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed Eng Lett 8(1):41–57. https://doi.org/10.1007/s13534-017-0047-y

    Article  PubMed  Google Scholar 

  9. Gayathri S, Gopi VP, Palanisamy P (2020) Automated classification of diabetic retinopathy through reliable feature selection. Phys Eng Sci Med 43(3):927–945. https://doi.org/10.1007/s13246-020-00890-3

    Article  CAS  PubMed  Google Scholar 

  10. Vijayan T, Sangeetha M, Kumaravel A, Karthik B (2020) Gabor filter and machine learning based diabetic retinopathy analysis and detection. Microprocess Microsyst. https://doi.org/10.1016/j.micpro.2020.103353

    Article  Google Scholar 

  11. Mateen M, Wen J, Nasrullah, Song S, Huang Z (2019) Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry (Basel). https://doi.org/10.3390/sym11010001

    Article  Google Scholar 

  12. Gayathri S, Gopi VP, Palanisamy P (2020) A lightweight CNN for diabetic retinopathy classification from fundus images. Biomed Signal Process Control 62:102115

    Article  Google Scholar 

  13. Gayathri S, Krishna AK, Gopi VP, Palanisamy P (2020) Automated binary and multi-class classification of diabetic retinopathy using haralick and multiresolution features. IEEE Access 8:57497–57504

    Article  Google Scholar 

  14. Gayathri S, Gopi VP, Palanisamy P (2021) Diabetic retinopathy classification based on multipath CNN and machine learning classifiers. Phys Eng Sci Med 44:639–653

    Article  CAS  Google Scholar 

  15. Welikala RA et al (2015) Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy. Comput Med Imaging Graph 43:64–77. https://doi.org/10.1016/j.compmedimag.2015.03.003

    Article  CAS  PubMed  Google Scholar 

  16. Jinfeng G, Qummar S, Junming Z, Ruxian Y, Khan FG (2020) Ensemble framework of deep CNNs for diabetic retinopathy detection. Comput Intell Neurosci. https://doi.org/10.1155/2020/8864698

    Article  PubMed  PubMed Central  Google Scholar 

  17. Shanthi T, Sabeenian RS (2019) Modified Alexnet architecture for classification of diabetic retinopathy images. Comput Electr Eng 76:56–64. https://doi.org/10.1016/j.compeleceng.2019.03.004

    Article  Google Scholar 

  18. Kassani SH, Kassani PH, Khazaeinezhad R, Wesolowski MJ, Schneider KA, Deters R (2019) Diabetic retinopathy classification using a modified xception architecture. 2019 IEEE international symposium on signal processing and information technology (ISSPIT). IEEE, Ajman. https://doi.org/10.1109/ISSPIT47144.2019.9001846

    Chapter  Google Scholar 

  19. Patel R, Chaware A (2020) Transfer learning with fine-tuned MobileNetV2 for diabetic retinopathy. 2020 international conference for emerging technology (INCET). IEEE, Belgaum. https://doi.org/10.1109/INCET49848.2020.9154014

    Chapter  Google Scholar 

  20. Gangwar AK, Ravi V (2021) Diabetic retinopathy detection using transfer learning and deep learning. Adv Intell Syst Comput 1176:679–689. https://doi.org/10.1007/978-981-15-5788-0_64

    Article  Google Scholar 

  21. Liu H, Yue K, Cheng S, Pan C, Sun J, Li W (2020) Hybrid model structure for diabetic retinopathy classification. J Healthc Eng. https://doi.org/10.1155/2020/8840174

    Article  PubMed  PubMed Central  Google Scholar 

  22. Qummar S et al (2019) A deep learning ensemble approach for diabetic retinopathy detection. IEEE Access 7:150530–150539. https://doi.org/10.1109/ACCESS.2019.2947484

    Article  Google Scholar 

  23. Jiang H, Yang K, Gao M, Zhang D, Ma H, Qian W (2019) An interpretable ensemble deep learning model for diabetic retinopathy disease classification. 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, Berlin, pp 2045–2048. https://doi.org/10.1109/EMBC.2019.8857160

    Chapter  Google Scholar 

  24. “Kaggle, Inc. Diabetic retinopathy detection vol. (2016). Available at https://www.kaggle.com/c/diabetic-retinopathy-detection

  25. Porwal P et al (2018) Indian diabetic retinopathy image dataset (IDRiD). IEEE Dataport. https://doi.org/10.21227/H25W98

    Article  Google Scholar 

  26. Decencière E et al (2014) Feedback on a publicly distributed image database: the Messidor database. Image Anal Stereol 33(3):231–234. https://doi.org/10.5566/ias.1155

    Article  Google Scholar 

  27. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Piscataway, pp 2818–2826. https://doi.org/10.1109/CVPR.2016.308

    Chapter  Google Scholar 

  28. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Piscataway, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

    Chapter  Google Scholar 

  29. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd international conference on learning representations, ICLR 2015—conference track proceedings. pp. 1–14

  30. Ding C, Peng H (2003) Minimum redundancy feature selection from microarray gene expression data. In: Proceedings of the 2003 IEEE Bioinformatics Conference: CSB 2003. pp. 523–528. https://doi.org/10.1109/CSB.2003.1227396.

  31. Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization

  32. Kononenko I (1994) Estimating attributes : analysis and extensions of RELIEF

  33. ME Corporation, Hence J (1992) The feature selection problem: traditional methods and a new algorithm

  34. Wan S, Liang Y, Zhang Y (2018) Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput Electr Eng 72:274–282. https://doi.org/10.1016/j.compeleceng.2018.07.042

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India under the Grant No. SRG/2020/000617.

Author information

Authors and Affiliations

  1. Bio-Medical Imaging Laboratory (BIOMIL), Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India

    N. Jagan Mohan, R. Murugan & Tripti Goel

  2. Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD, 4006, Australia

    Seyedali Mirjalili

  3. Yonsei Frontier Lab, Yonsei University, Seoul, Korea

    Seyedali Mirjalili

  4. Department of Ophthalmology, Silchar Medical College and Hospital, Silchar, Assam, 788014, India

    Parthapratim Roy

Authors
  1. N. Jagan Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Murugan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tripti Goel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seyedali Mirjalili
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Parthapratim Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Murugan.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

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

Informed consent

There is no individual participant included in the study.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jagan Mohan, N., Murugan, R., Goel, T. et al. A novel four-step feature selection technique for diabetic retinopathy grading. Phys Eng Sci Med 44, 1351–1366 (2021). https://doi.org/10.1007/s13246-021-01073-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-021-01073-4

Keywords

Search

Navigation