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Journal of Medical Systems

, 42:247 | Cite as

Diabetic Retinopathy Diagnosis from Retinal Images Using Modified Hopfield Neural Network

  • D. Jude Hemanth
  • J. Anitha
  • Le Hoang SonEmail author
  • Mamta Mittal
Image & Signal Processing
  • 78 Downloads
Part of the following topical collections:
  1. Image & Signal Processing

Abstract

Disease diagnosis from medical images has become increasingly important in medical science. Abnormality identification in retinal images has become a challenging task in medical science. Effective machine learning and soft computing methods should be used to facilitate Diabetic Retinopathy Diagnosis from Retinal Images. Artificial Neural Networks are widely preferred for Diabetic Retinopathy Diagnosis from Retinal Images. It was observed that the conventional neural networks especially the Hopfield Neural Network (HNN) may be inaccurate due to the weight values are not adjusted in the training process. This paper presents a new Modified Hopfield Neural Network (MHNN) for abnormality classification from human retinal images. It relies on the idea that both weight values and output values can be adjusted simultaneously. The novelty of the proposed method lies in the training algorithm. In conventional method, the weights remain fixed but the weights are changing in the proposed method. Experimental performed on the Lotus Eye Care Hospital containing 540 images collected showed that the proposed MHNN yields an average sensitivity and specificity of 0.99 and accuracy of 99.25%. The proposed MHNN is better than HNN and other neural network approaches for Diabetic Retinopathy Diagnosis from Retinal Images.

Keywords

Hopfield neural network Retinal images Disease diagnosis Classification accuracy 

Notes

Acknowledgements

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2018.02.

Compliance with ethical standards

The authors declare that they do not have any conflict of interests. This research does not involve any human or animal participation. All authors have checked and agreed with the submission.

References

  1. 1.
    Zhou, C., Chase, J. G., Ismail, H., Signal, M. K., Haggers, M., Rodgers, G. W., and Pretty, C., Silicone phantom validation of breast cancer tumor detection using nominal stiffness identification in digital imaging elasto-tomography (DIET). Biomedical Signal Processing and Control 39:435–447, 2018.CrossRefGoogle Scholar
  2. 2.
    Sollie, A., Sijmons, R. H., Helsper, C., and Numans, M. E., Reusability of coded data in the primary care electronic medical record: A dynamic cohort study concerning cancer diagnoses. Int. J. Med. Inform. 99:45–52, 2017.CrossRefGoogle Scholar
  3. 3.
    Jiang, J., Trundle, P., and Ren, J., Medical image analysis with artificial neural networks. Comput. Med. Imaging Graph. 34(8):617–631, 2010.CrossRefGoogle Scholar
  4. 4.
    Zaki, W. M. D. W., Zulkifley, M. A., Hussain, A., Halim, W. H. W., Mustafa, N. B. A., and Ting, L. S., Diabetic retinopathy assessment: Towards an automated system. Biomedical Signal Processing and Control 24:72–82, 2016.CrossRefGoogle Scholar
  5. 5.
    Fang, Y., Zhao, X., Tan, Z., and Xiao, W., Network Embedding via a Bi-Mode and Deep Neural Network Model. Symmetry 10(5):180, 2018.CrossRefGoogle Scholar
  6. 6.
    Zheng, H. T., Chen, J. Y., Yao, X., Sangaiah, A. K., Jiang, Y., and Zhao, C. Z., Clickbait Convolutional Neural Network. Symmetry 10(5):138, 2018.CrossRefGoogle Scholar
  7. 7.
    Luculescu, M. C., and Lache, S., Computer-aided diagnosis system for retinal diseases in medical imaging. WSEAS Trans Syst 7:264–276, 2008.Google Scholar
  8. 8.
    Grewal, D. S., Jain, R., Grewal, S. P. S., and Rihani, V., Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis. Eur. J. Ophthalmol. 18(6):915, 2008.CrossRefGoogle Scholar
  9. 9.
    Karthikeyan, R., and Alli, P., Retinal image analysis for abnormality detection-an overview. J. Comput. Sci. 8(3):436, 2012.CrossRefGoogle Scholar
  10. 10.
    Kavitha, G., and Ramakrishnan, S., Abnormality detection in retinal images using ant colony optimization and artificial neural networks-biomed 2010. Biomed. Sci. Instrum. 46:331–336, 2010.PubMedGoogle Scholar
  11. 11.
    Shaeidi, A., An algorithm for identification of retinal microaneurysms. J Serbian Soc Comput Mech 4:43–51, 2010.Google Scholar
  12. 12.
    Baroni, M., Fortunato, P., Pollazzi, L., and La Torre, A., Multiscale Filtering and Neural Network Classification for Segmentation and Analysis of Retinal Vessels. Webmed Central Biomedical engineering 3(7):wmc003588, 2012.  https://doi.org/10.9754/journal.wmc.2012.003588.CrossRefGoogle Scholar
  13. 13.
    Lim, G., Lee, M. L., Hsu, W., & Wong, T. Y., Transformed Representations for Convolutional Neural Networks in Diabetic Retinopathy Screening. In: AAAI Workshop: Modern Artificial Intelligence for Health Analytics, pp. 21–25, 2014.Google Scholar
  14. 14.
    Somfai, G. M., Tátrai, E., Laurik, L., Varga, B., Ölvedy, V., Jiang, H. et al., Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes. BMC bioinformatics 15(1):106, 2014.CrossRefGoogle Scholar
  15. 15.
    Yun, W. L., Acharya, U. R., Venkatesh, Y. V., Chee, C., Min, L. C., and Ng, E. Y. K., Identification of different stages of diabetic retinopathy using retinal optical images. Inf. Sci. 178(1):106–121, 2008.CrossRefGoogle Scholar
  16. 16.
    Fei, Y., Hu, J., Gao, K., Tu, J., Li, W. Q., and Wang, W., Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models. J. Crit. Care 39:115–123, 2017.CrossRefGoogle Scholar
  17. 17.
    Perova, I., and Bodyanskiy, Y., Fast medical diagnostics using autoassociative neuro-fuzzy memory. International Journal of Computing 16(1):34–40, 2017.Google Scholar
  18. 18.
    Rajasekaran, S., and Pai, G. V., Neural Networks, Fuzzy Systems and Evolutionary Algorithms:Synthesis and Applications. PHI Learning Pvt. Ltd. 2nd edition. Delhi, India, 2017.Google Scholar
  19. 19.
    Heilbronner, R., and Barrett, S., Digital Image Processing. In: Image Analysis in Earth Sciences (pp. 31–57). Berlin Heidelberg: Springer, 2014.CrossRefGoogle Scholar
  20. 20.
    Jude Hemanth, D., Anitha, J., and Indumathy, A., Diabetic Retinopathy Diagnosis in Retinal Images Using Hopfield Neural Network. IETE J. Res. 62(6):893–900, 2016.CrossRefGoogle Scholar
  21. 21.
    Jha, S., Kumar, R., Chatterjee, J. M., Khari, M., Yadav, N., and Smarandache, F., Neutrosophic softset decision making for stock trending analysis. Evol. Syst., 1–7, 2018.  https://doi.org/10.1007/s12530-018-9247-7.
  22. 22.
    Dey, A., Broumi, S., Bakali, A., Talea, M., and Smarandache, F., A new algorithm for finding minimum spanning trees with undirected neutrosophic graphs. Granular Computing, 1–7, 2018.  https://doi.org/10.1007/s41066-018-0084-7.
  23. 23.
    Ali, M., Son, L. H., Khan, M., and Tung, N. T., Segmentation of dental X-ray images in medical imaging using neutrosophic orthogonal matrices. Expert Syst. Appl. 91:434–441, 2018.CrossRefGoogle Scholar
  24. 24.
    Ali, M., Dat, L. Q., Son, L. H., and Smarandache, F., Interval complex neutrosophic set: formulation and applications in decision-making. International Journal of Fuzzy Systems 20(3):986–999, 2018.CrossRefGoogle Scholar
  25. 25.
    Nguyen, G. N., Ashour, A. S., and Dey, N., A survey of the state-of-the-arts on neutrosophic sets inbiomedical diagnoses. Int. J. Mach. Learn. Cybern., 1–13, 2017.  https://doi.org/10.1007/s13042-017-0691-7.
  26. 26.
    Ali, M., Son, L. H., Deli, I., and Tien, N. D., Bipolar neutrosophic soft sets and applications in decision making. J. Intell. Fuzzy Syst. 33(6):4077–4087, 2017.CrossRefGoogle Scholar
  27. 27.
    Thanh, N. D., Son, LH, and Ali, M., Neutrosophic recommender system for medical diagnosis based onalgebraic similarity measure and clustering. In: Fuzzy Systems (FUZZ-IEEE), 2017 IEEE International Conference on (pp. 1–6). IEEE, 2017.Google Scholar
  28. 28.
    Wu, H., Wei, Y., Shang, Y., Shi, W., Wang, L., Li, J. et al., iT2DMS: a Standard-Based Diabetic Disease Data Repository and its Pilot Experiment on Diabetic Retinopathy Phenotyping and Examination Results Integration. J. Med. Syst. 42(7):131, 2018.CrossRefGoogle Scholar
  29. 29.
    Somasundaram, S. K., and Alli, P., A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy. J. Med. Syst. 41(12):201, 2017.CrossRefGoogle Scholar
  30. 30.
    Somu, N., Raman, M. G., Kirthivasan, K., and Sriram, V. S., Hypergraph based feature selection technique for medical diagnosis. J. Med. Syst. 40(11):239, 2016.CrossRefGoogle Scholar
  31. 31.
    Carmen, V., Maria, G., Roberto, H., and Maria, L., Automated detection of diabetic retinopathy in retinal images. Indian Journal of Opthalmology 64(1):26–32, 2016.CrossRefGoogle Scholar
  32. 32.
    Morten, B. H. et al., Results of automated retinal image analysis for detection of diabetic retinopathy from the Nakuru study, Kenya. Plos One 10(10):e0139148, 2015.CrossRefGoogle Scholar
  33. 33.
    Tanthuwapathom, R., and Hnoohom, N., Detection of Diabetic Retinopathy Using ImageProcessing. In: International Symposium on Natural Language Processing, pp. 259–265: Springer, Cham, 2016.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of ECEKarunya UniversityCoimbatoreIndia
  2. 2.VNU Information Technology InstituteVietnam National UniversityHanoiVietnam
  3. 3.G.B. Pant Govt. Engineering CollegeNew DelhiIndia

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