Skip to main content

Advertisement

SpringerLink
Log in

A Fuzzy Approach to Determining Critical Factors of Diabetic Retinopathy and Enhancing Data Classification Accuracy

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Diabetic retinopathy (DR) is a chronic and progressive sight-threatening complication of retinal microvasculature associated with diabetes mellitus. These morphological subtle variations in the retina can result in visual impairment or eventual vision loss if not treated in time. The early detection and diagnosis of DR is mandatory to safeguard the patient’s vision. To investigate and intervene at an earlier stage, we aim to systematically extract and analyze different DR factors, such as retinal condition of eyes, presence of microaneurysms (MAs), MAs with exudate, diameter of optical disk (OD), and Euclidian distance between macula and center of the OD. Considering the DR attributes, first we find out the important attributes of DR by applying fuzzy analytical network process to rank attributes from the most to the least related factors to DR. Then, a transformed fuzzy neural network is created to enhance the classification accuracy. Finally, we extract association rules among the selected attributes of DR to reveal their importance and degree of severity. Findings of this study reveal a new perspective on the treatment of DR at the early stage and prevention from any complications to improve the quality of life of all people.

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

References

  1. WHO: Diabetes. https://www.who.int/news-room/fact-sheets/detail/diabetes. Accessed on 20 Oct 2018

  2. Healthline: The Effects of Diabetes on Your Body. https://www.healthline.com/health/diabetes/effects-on-body#1. Accessed on 22 Oct 2018

  3. Lee, R., Wong, T.Y., Sabanayagam, C.: Epidemiology of diabetic retinopathy, diabetic macular edema, and related vision loss. Eye Vis. 2(17), 1–25 (2015)

    Google Scholar 

  4. Texas Retina Association: What You Should Know About Nonproliferative Diabetic Retinopathy (NPDR). http://www.texasretina.com/2018/11/01/what-you-should-know-about-nonproliferative-diabetic-retinopathy-npdr. Accessed on 20 Oct 2018

  5. American Optometric Association: Diabetic Retinopathy. https://www.aoa.org/patients-and-public/eye-and-vision-problems/glossary-of-eye-and-vision-conditions/diabetic-retinopathy. Accessed on 20 Oct 2018

  6. Wah, W., Earnest, A., Sabanayagam, C., Cheng, C.-Y., Ong, M.E.H., Wong, T.Y., Lamoureux, E.L.: Composite measures of individual and area-level socio-economic status are associated with visual impairment in Singapore. PLoS ONE 10(11), e0142302 (2015)

    Article  Google Scholar 

  7. Kahloun, R., Jelliti, B., Zaouali, S., Attia, S., Yahia, S.B., Resnikoff, S., Khairallah, M.: Prevalence and causes of visual impairment in diabetic patients in Tunisia. Eye 28(8), 986–991 (2014)

    Article  Google Scholar 

  8. Van de Put, M.A.J., Hoeksema, L., Wanders, W., Nolte, I.M., Hooymans, J.M.M., Los, L.I.: Postoperative vision-related quality of life in macula-off rhegmatogenous retinal detachment patients and its relation to visual function. PLoS ONE 9(12), e114489 (2014)

    Article  Google Scholar 

  9. The Best Possible Care: Diabetic Retinopathy. https://retinapittsburgh.com/2017/11/diabetic-retinopathy/. Accessed on 20 Oct 2018

  10. 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(3), 123–234 (2014)

    Article  MATH  Google Scholar 

  11. Huang, J.J.: A matrix method for the fuzzy analytic network process. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 16(6), 863–878 (2008)

    Article  MATH  Google Scholar 

  12. Isfahani, S.N., Haddad, A.A., Roghanian, E., Rezayi, M.: Customer relationship management performance measurement using balanced scorecard and fuzzy analytic network process. J. Intell. Fuzzy Syst. 27(1), 377–389 (2014)

    Google Scholar 

  13. Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1980)

    MATH  Google Scholar 

  14. Bellman, R.E., Zadeh, L.A.: Decision-making in a fuzzy environment. Manag. Sci. 17(4), 141–164 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  15. Akaa, O.U., Abu, A., Spearpoint, M., Giovinazzi, S.: A group-AHP decision analysis for the selection of applied fire protection to steel structures. Fire Saf. J. 86, 95–105 (2016)

    Article  Google Scholar 

  16. Asan, U., Soyer, A., Serdarasan, S.: A fuzzy analytic network process approach. Comput. Intell. Syst. Ind. Eng. 6(2), 155–179 (2012)

    Google Scholar 

  17. Feng, X.Q., Wei, C., Liu, Q.: EDAS method for extended hesitant fuzzy linguistic multicriteria decision making. Int. J. Fuzzy Syst. 20(6), 1–14 (2018)

    MathSciNet  Google Scholar 

  18. Chutia, R., Mahanta, S., Datta, D.: Arithmetic of triangular fuzzy variable from credibility theory. Int. J. Energy Inf. Commun. 2(3), 73–80 (2011)

    Google Scholar 

  19. Sharma, H.K.: A fuzzy logic multi-criteria decision approach for vendor selection manufacturing system. IEEE Trans. Mod. Eng. Res. 2(6), 4189–4194 (2012)

    Google Scholar 

  20. Abdullah, L., Najib, L.: A new preference scale MCDM method based on interval-valued intuitionistic fuzzy sets and the analytic hierarchy process. Soft. Comput. 20(2), 511–523 (2016)

    Article  Google Scholar 

  21. Sehra, K., Brar, Y.S., Kaur, N.: Multi criteria decision making approach for selecting effort estimation model. Int. J. Comput. Appl. 39(1), 10–17 (2012)

    Google Scholar 

  22. Chu, T.C., Varma, R.: Evaluating suppliers via a multiple levels multiple criteria decision making method under fuzzy environment. IEEE Trans. Comput. Ind. Eng. 62(2), 653–660 (2012)

    Article  Google Scholar 

  23. Huang, C.-M., Ghafoor, Y., Huang, Y.-P., Liu, S.-I.: A dolphin herding inspired fuzzy data clustering model and its applications. Int. J. Fuzzy Syst. 18(2), 299–311 (2016)

    Article  Google Scholar 

  24. Ma, Y., Wu, X.: A new approach for deriving fuzzy global priorities in fuzzy analytic network process. J. Intell. Fuzzy Syst. 30(2), 1249–1261 (2016)

    Article  MATH  Google Scholar 

  25. Huang, Y.-P., Singh, A., Liu, S.-I., Wu, S.-I., Quoc, H.A., Sereter, A.: Developing transformed fuzzy neural networks to enhance medical data classification accuracy. Int. J. Fuzzy Syst. 20(6), 1925–1937 (2018)

    Article  Google Scholar 

  26. Antel, B., Hajdu, A.: Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods. Pattern Recogn. 45(1), 264–270 (2012)

    Article  Google Scholar 

  27. Akram, M.U., Khalid, S., Tariq, A., Khan, S.A., Azam, F.: Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput. Biol. Med. 45, 161–171 (2014)

    Article  Google Scholar 

  28. Navarro, P.J., Alonso, D., Stathis, K.: Automatic detection of microaneurysms in diabetic retinopathy fundus images using the L * a * b color space. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 33(1), 74–83 (2016)

    Article  Google Scholar 

  29. Seoud, L., Hurtut, T., Chelbi, J., Cheriet, F., Langlois, J.M.P.: Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Trans. Med. Imaging 35(4), 1116–1126 (2016)

    Article  Google Scholar 

  30. Li, X., Pang, T., Xiong, B., Liu, W., Liang, P., Wang, T.: Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. In: Proceedings of the 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Shanghai, China, pp. 1–11 (2017)

  31. Lam, C., Yi, D., Guo, M., Lindsey, T.: Automated detection of Diabetic retinopathy using deep learning. In: AMIA Summits on Translational Science Proceedings, pp. 147–155 (2018)

Download references

Acknowledgements

This study was funded in part by the Ministry of Science and Technology, Taiwan, under Grants MOST108-2321-B-027-001, MOST107-2221-E-027-113 and MOST106-2221-E-027-001 and by a joint project between the National Taipei University of Technology and the Chang Gung Memorial Hospital under Grant NTUT-CGMH-106-05.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan

    Yo-Ping Huang, Haobijam Basanta & Tzu-Hao Wang

  2. Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, 23741, Taiwan

    Yo-Ping Huang

  3. Department of Neurology, Chang Gung Memorial Hospital, Linkou, Taoyuan, 33333, Taiwan

    Hung-Chou Kuo

  4. Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taoyuan, 33333, Taiwan

    Wei-Chi Wu

  5. College of Medicine, Chang Gung University, Taoyuan, 33333, Taiwan

    Wei-Chi Wu

Authors
  1. Yo-Ping Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haobijam Basanta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tzu-Hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hung-Chou Kuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei-Chi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yo-Ping Huang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, YP., Basanta, H., Wang, TH. et al. A Fuzzy Approach to Determining Critical Factors of Diabetic Retinopathy and Enhancing Data Classification Accuracy. Int. J. Fuzzy Syst. 21, 1844–1857 (2019). https://doi.org/10.1007/s40815-019-00668-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-019-00668-0

Keywords

Search

Navigation