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BPSO-Based Feature Selection for Precise Class Labeling of Diabetic Retinopathy Images

  • Rahul Kumar ChaurasiyaEmail author
  • Mohd Imroze Khan
  • Deeksha Karanjgaokar
  • B. Krishna Prasanna
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 949)

Abstract

Diabetic retinopathy (DR) is an eye disease caused by high levels of blood sugar in diabetic patients. A timely detection and diagnosis of DR with the aid of powerful algorithms applied to the eye fundus images may minimize the risk of complete vision loss. Several previously employed techniques concentrate on the extraction of the relevant retinal components characteristic to DR using texture and morphological features. In this study, we propose to estimate the severity of DR by classification of eye fundus images based on texture features into two classes using a support vector machine (SVM) classifier. We also introduce a reliable technique for optimal feature selection to improve the classification accuracy offered by an SVM classifier. We have applied the Wilcoxon signed rank test and observed the p-values to be 3.7380 × 10−5 for Set 1 and 7.7442 × 10−6 for Set 2. These values successfully reject the null-hypothesis against the p-value benchmark of 5%, indicating that the performance of SVM has significantly improved when used in combination with an optimization algorithm. Overall results suggest a reliable and accurate classification of diabetic retinopathy images that could be helpful in the quick and reliable diagnosis of DR.

Keywords

Diabetic retinopathy Texture features Binary particle swarm optimization (BPSO) Class labeling Cross-validation 

References

  1. 1.
    Mishra, P.K., Sinha, A., Teja, K.R., Bhojwani, N., Sahu, S., Kumar, A.: A computational modeling for the detection of diabetic retinopathy severity. Bioinformation 10, 556 (2014)CrossRefGoogle Scholar
  2. 2.
    Cai, X., McGinnis, J.F.: Diabetic retinopathy: animal models, therapies, and perspectives. J. Diabetes Res. (2016)Google Scholar
  3. 3.
    Aiello, L.M., Cavallerano, J., Aiello, L.P., Bursell, S.E., Guyer, D.R., Yannuzzi, L.A., Chang, S.: Diabetic retinopathy. In: Retina Vitreous Macula, vol. 2 (1999)Google Scholar
  4. 4.
    Benson, W.E., Tasman, W., Duane, T.D.: Diabetes mellitus and the eye. In: Duane’s Clinical Ophthalmology, vol. 3 (1994)Google Scholar
  5. 5.
    Shaw, J.E., Sicree, R.A., Zimmet, P.Z.: Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res. Clin. Pract. 87, 4–14 (2010)CrossRefGoogle Scholar
  6. 6.
    Chobanian, A.V.: Control of hypertension—an important national priority. Mass. Medical Soc. (2001)Google Scholar
  7. 7.
    Thomas, R.L., Dunstan, F., Luzio, S.D., Chowdury, S.R., Hale, S., North, R.V., Gibbins, R., Owens, D.R.: Incidence of diabetic retinopathy in people with type 2 diabetes mellitus attending the Diabetic Retinopathy Screening Service for Wales: retrospective analysis. BMJ 344, e874 (2012)CrossRefGoogle Scholar
  8. 8.
    Raman, R., Rani, P.K., ReddiRachepalle, S., Gnanamoorthy, P., Uthra, S., Kumaramanickavel, G., Sharma, T.V.: Prevalence of diabetic retinopathy in India: Sankaranethralaya diabetic retinopathy epidemiology and molecular genetics study report 2. Ophthalmology 116 (2009)CrossRefGoogle Scholar
  9. 9.
    Cedrone, C., Mancino, R., Cerulli, A., Cesareo, M., Nucci, C.: Epidemiology of primary glaucoma: prevalence, incidence, and blinding effects. Prog. Brain Res. 173, 3–14 (2008)CrossRefGoogle Scholar
  10. 10.
    George, R., Ramesh, S.V., Vijaya, L.: Glaucoma in India: estimated burden of disease. J. Glaucoma 19, 391–397 (2010)CrossRefGoogle Scholar
  11. 11.
    Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., Gain, P., Ordonez, R., Massin, P., Erginay, A., Charton, B., Klein, J.-C.: Feedback on a publicly distributed image database: the Messidor database. Image Anal Stereol 33(4) (2014)CrossRefGoogle Scholar
  12. 12.
    Zhang, Y., Wu, X., Lu, S., Wang, H., Phillips, P., Wang, S.: Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization. Simulation 92, 873–885 (2016)CrossRefGoogle Scholar
  13. 13.
    Du, N., Li, Y.: Automated identification of diabetic retinopathy stages using support vector machine. In: Conference Automated identification of Diabetic Retinopathy Stages Using Support Vector Machine, pp. 3882–3886. IEEE (2013) Google Scholar
  14. 14.
    Zhang, Y., Yang, J., Wang, S., Dong, Z., Phillips, P.: Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. J. Exp. Theor. Artif. Intell. 29, 299–312 (2017)CrossRefGoogle Scholar
  15. 15.
    Zhang, Y., Wang, S., Sun, P., Phillips, P.: Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-Med. Mater. Eng. 26, S1283–S1290 (2015)CrossRefGoogle Scholar
  16. 16.
    Xu, L., Luo, S.: Support vector machine based method for identifying hard exudates in retinal images. In: Conference Support Vector Machine Based Method for Identifying Hard Exudates in Retinal Images, pp. 138–141. IEEE (2009)Google Scholar
  17. 17.
    Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2011)Google Scholar
  18. 18.
    Chaurasiya, R.K., Londhe, N.D., Ghosh, S.: Binary DE-based channel selection and weighted ensemble of SVM classification for novel brain–computer interface using Devanagari Script-based P300 speller paradigm. Int. J. Hum.-Comput. Interact. 32, 861–877 (2016)CrossRefGoogle Scholar
  19. 19.
    Wang, S., Phillips, P., Yang, J., Sun, P., Zhang, Y.: Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients. Biomed. Eng./Biomedizinische Technik 61, 431–441 (2016)CrossRefGoogle Scholar
  20. 20.
    Blankertz, B., Muller, K.-R., Krusienski, D.J., Schalk, G., Wolpaw, J.R., Schlogl, A., Pfurtscheller, G., Millan, J.R., Schroder, M., Birbaumer, N.: The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 153–159 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Rahul Kumar Chaurasiya
    • 1
    Email author
  • Mohd Imroze Khan
    • 1
  • Deeksha Karanjgaokar
    • 1
  • B. Krishna Prasanna
    • 1
  1. 1.Department of Electronics and TelecommunicationNational Institute of TechnologyRaipurIndia

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