Fuzzy Deep Learning for Diabetes Detection

  • Tushar Deshmukh
  • H. S. FadewarEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)


The use of science for the betterment of society is the main cause for research for years. That is the reason the framework of diabetes diagnosis is always changing with new dimensions. The new and advance algorithms on the horizons are tried in hope of getting better accuracy and speed. Apart from normal algorithms researchers have tried the possible hybrid combinations. In recent times, the Convolution Neural Network (CNN) has outperformed most of the application areas of traditional prediction algorithms. Here is an attempt to use the deep convolutional neural network for diagnosis of diabetes. This work has two major contributions, first is the application of CNN for diabetes detection and second is data fuzzification in matrix form to suit needs of CNN. In the experiments, the comparison is made between classical NN and CNN for diabetes detection. Results prove that fuzzification of data significantly improves the accuracy of CNN and CNN outperforms classical NN.


Deep learning Convolutional neural network Fuzzy deep learning Classification Diabetes detection 


  1. 1.
    American Diabetes Association: Diagnosis and classification of diabetes mellitus. Diabetes Care 37(1), s81–s90 (2014)Google Scholar
  2. 2.
    Diagnosis and classification of diabetes mellitus: new criteria. Am. Fam. Physician 58(6), 1355–1362 (1998)Google Scholar
  3. 3.
    Ross, T.: Fuzzy Logic with Engineering Applications, 3 edn. Wiley Student EditionGoogle Scholar
  4. 4.
    Kumar, S.: Neural Networks: A Classroom Approach, 2 edn. Tata McGraw-Hill Education (2012)Google Scholar
  5. 5.
    Rajasekaran, S., Pai, G.A.V.: Neural Networks, Fuzzy Logic, and Genetic Algorithms Synthesis and Applications, 8 edn. PHI (2003)Google Scholar
  6. 6.
    Gulshan, V., Peng, L., Coram, M.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)CrossRefGoogle Scholar
  7. 7.
    Pinheiro, P., Collobert, R.: Recurrent convolutional neural networks for scene labeling. Proc. Mach. Learn. Res. 32(1), 82–90 (2014)Google Scholar
  8. 8.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kamble, T.P., Patil, S.T.: Diabetes detection using deep learning approach. Int. J. Innov. Res. Sci. Technol. 2(12), 342–349 (2016)Google Scholar
  10. 10.
    Coenen, F., Broadbent, D.M., Harding, S.P., Zheng, Y., Pratt, H.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. (2016)Google Scholar
  11. 11.
    Yamini, C., Tharani, S.: Classification using convolutional neural network for heart and diabetics datasets. Int. J. Adv. Res. Comput. Commun. Eng. 5(12), 417–422 (2016)Google Scholar
  12. 12.
    Asati, V.C.S.: Automatic detection of diabetic retinopathy using deep convolutional neural network. Int. J. Adv. Res. Ideas Innov. Technol. 3, 633–641 (2017)Google Scholar
  13. 13.
    Liu, K., Lai, S., Zhou, G., Zhao, J., Zeng, D.: Relation classification via convolutional deep neural network. In: National Laboratory of Pattern Classification (2014)Google Scholar
  14. 14.
    Karen Simonyan, Z.: Very deep convolutional neural network for large scale image recognition. ICLR (2015)Google Scholar
  15. 15.
    Fadewar, H.S., Deshmukh, T.: Machine predicts the diagnosis a brief review of medical diagnosis by machine learning techniques. Indian J. Comput. Sci. Eng. 8(5), 636–638 (2017)Google Scholar
  16. 16.
    Fadewar, H., Deshmukh, T.: Data mining techniques for diagnosis of diabetes: a review. Int. J. Emerg. Res. Manag. Technol. 6(9), 212–214 (2017)Google Scholar
  17. 17.
  18. 18.
    Deshpande, P., Iyer, B.: Research directions in the Internet of Every Things (IoET). In: International Conference on Computing, Communication and Automation (ICCCA), pp. 1353–1357 (2017)Google Scholar
  19. 19.
    Patil, M., Iyer, B., Arya, R.: Performance evaluation of PCA and ICA algorithm for facial expression recognition application. In: Proceedings of Fifth International Conference on Soft Computing for Problem Solving, pp. 965–976 (2016)Google Scholar
  20. 20.
    Chandore, V., Asati, S.: Automatic detection of diabetic retinopathy using deep convolutional neural network. Int. J. Adv. Res. Ideas Innov. Technol. 3(4), 633–641 (2017)Google Scholar
  21. 21.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computational SciencesSRTMUNandedIndia

Personalised recommendations