A New Method Based-Gentle Adaboost and Wavelet Transform for Breast Cancer Classification

  • Nezha Hamdi
  • Khalid AuhmaniEmail author
  • Moha Mrabet Hassani
  • Omar Elkharki
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 914)


In this paper we have realized a comparative study of mammograms classification accuracy based on a new Gentle Adaboost algorithm for different wavelet transforms and different features. Our proposition deals with the combination of a new Gentle Adaboost based algorithm with three wavelets transforms. In this new algorithm, the main classifier is realized by weighted weak classifiers. These weak classifiers are constructed from the sub-bands of discrete wavelet transform, stationary wavelet transform and double density wavelet transform. Used features are extracted from transformed mammograms. We have investigated the effect of these wavelet transforms combined with the extracted features on the classification accuracy. Receiver Operating Curves (ROC) tool is employed to evaluate the performance of the propositions. Mammograms of MIAS Database are used as samples to classify. True positive rate is plotted versus false positive rate for different types of features and for Gentle Adaboost iterations. Results showed that the best area under curve (AUC), is reached for Zernike moments combined with double density wavelet transform and it is equal to 1 for both t = 10 and t = 50.


Artificial intelligence Image processing Machine learning Gentle Adaboost Wavelet transform ROC Feature extraction 


  1. 1.
    Freund, Y.: Boosting a weak learning algorithm by majority. Inf. Comput. 121(2), 256–285 (1995)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Meir, R., Ratsch, G.: An introduction to boosting and leveraging. In: Lecture Notes in Artificial Intelligence, pp. 118–183 (2003)CrossRefGoogle Scholar
  3. 3.
    Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990)Google Scholar
  4. 4.
    Schapire, R.E.: The boosting approach to machine learning: an overview. In: Proceedings of the MSRI Workshop on Nonlinear Estimation and Classification (2002)Google Scholar
  5. 5.
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999)CrossRefGoogle Scholar
  6. 6.
    Hastie, T., Tibshirani, R., Friedman, J.: Elements of Statistical Learning: Data Mining and Inference and Prediction. Springer, New York (2001)CrossRefGoogle Scholar
  7. 7.
    Nanni, L., Lumini, A.: Wavelet selection for disease classification by DNA microarray data. Expert Syst. Appl. 38(1), 990–995 (2011)CrossRefGoogle Scholar
  8. 8.
    Freund, Y.: An adaptive version of the boost by majority algorithm. Mach. Learn. 43(3), 293–318 (2001)CrossRefGoogle Scholar
  9. 9.
    Vezhnevets, A.: Modest AdaBoost – teaching AdaBoost to generalize better. In: Paper presented at the Graphicon, Novosibirsk Akademgorodok, Russia, pp. 322–325 (2005)Google Scholar
  10. 10.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Annal. Stat. 28, 337–374 (2000)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Culp, M., Johnson, K., Michailidis, F.: ada: an R Package for Stochastic Boosting (2007)Google Scholar
  12. 12.
    Kanade, T., Jain, A., Ratha, N.K.: Audio-and video-based biometric person authentification. In: 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, 20–22 July (2005)Google Scholar
  13. 13.
    Lesecq, S., Gentil, S., Fagarasan, I.: Fault isolation based on wavelet transform. J. Control Eng. Appl. Inform. CEAI 9(3;4), 51–58 (2007)Google Scholar
  14. 14.
    Cioaca, T., Dumitrescu, B., Stupariu, M.-S.: Lazy wavelet simplication using scale-dependent dense geometric variability descriptors. J. Control Eng. Appl. Inform. CEAI 19(1), 15–26 (2017)Google Scholar
  15. 15.
    Hamdi, N., Auhmani, K., Hassani, M.M., Elkharki, O.: An efficient gentle adaboost-based approach for mammograms classification. J. Theor. Appl. Inf. Technol. 81(1) (2015)Google Scholar
  16. 16.
    Zhang, B.L., Zhang, H., Je, S.S.: Face recognition by applying subband representation and kernel associative memory. IEEE Trans. Neural Netw. 15, 166–177 (2004)CrossRefGoogle Scholar
  17. 17.
    Nason, G.P., Silverman, B.W.: The stationary wavelet transform and some statistical applications in Wavelets and Statistics. In: Lecture Notes in Statistics. Springer, New York (1995)CrossRefGoogle Scholar
  18. 18.
    Arivazhagan, S., Ganesan, L., Savithri, C.N.: Effective multi-resolution transform identification for characterization and classification of texture groups. ICTACT J. Image Video Processing, 02(02) (2011)Google Scholar
  19. 19.
    Gopi, V.P., Babu, V.S., Dilna, C.: Image resolution enhancement using undecimated double density wavelet transform. Signal Process. Int. J. (SPIJ) 8(5), 67 (2014)Google Scholar
  20. 20.
    Tamura, H., Mori, S., Yamawaki, T.: Texture features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. SMC 8(6), 460–473 (1978)CrossRefGoogle Scholar
  21. 21.
    Howarth, P., Rüger, S.: Evaluation of texture features for content-based image retrieval. In: Proceedings of the International Conference on Image and Video Retrieval (CIVR 2004), LNCS, vol. 3115, pp. 326–334, Dublin, Ireland (2004)Google Scholar
  22. 22.
    Deans, S.R.: Hough transform from the radon transform. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 3(2), 185–188 (1981)CrossRefGoogle Scholar
  23. 23.
    Murphy, L.M.: Linear feature detection and enhancement in noisy images via the Radon transform. Pattern Recognit. Lett. 4, 279–284 (1986)CrossRefGoogle Scholar
  24. 24.
    Suckling, J., et al.: The mammographic image analysis society digital mammogram database. In: Exerpta Medica International Congress Series, vol. 1069, pp. 375–378 (1994).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nezha Hamdi
    • 1
  • Khalid Auhmani
    • 2
    Email author
  • Moha Mrabet Hassani
    • 3
  • Omar Elkharki
    • 4
  1. 1.High Institute of Engineering and Business (ISGA)MarrakechMorocco
  2. 2.Department of Industrial Engineering, National School of Applied SciencesCadi Ayyad UniversitySafiMorocco
  3. 3.Department of Physics, Faculty of Sciences SemlaliaCadi Ayyad UniversityMarrakechMorocco
  4. 4.Faculty of Sciences and TechnologiesTangierMorocco

Personalised recommendations