Multi Intelligent Fuzzy Integration (Mifi) Support Vector Machines for the Mammogram Classification

  • Authors

    • G Jayandhi
    • Dr R.Dhaya
    • Dr R.Kanthavel
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.34.18978
  • GLCM, Extreme Fuzzy Support Vector Machines, MIFI, Saliency Maps, Micro calcification, ROI Segmentation.

  • Abstract

    Breast cancer is the most important problem across the globe in which the 80% of the women are suffering without knowing the causes and effects of the cancer cells. Mammogram Image is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation. Several intelligent   techniques are suggested   for the detection of Micro calcification in mammogram images. The new technique MIFI-SVM has been proposed which integrates the GLCM features along with the Fuzzy Support Vector Machines. ROI Segmentation using Saliency maps has been used for the proposed algorithm and  feature is extracted using GLCM and fed to Fuzzy Support Vector Machines   The  MIAS datasets has been used for testing the proposed algorithm and accuracy, sensitivity has been measured which proves to be better when  compared to other  Multi-level SVM’s, C-SVM and Neural Networks.

     

     

  • References

    1. [1] T.L. Economopoulos, P.A. Asvestas, G.K. Matsopoulos,“Contrast enhancement of images using Partitioned Iterated Function Systemsâ€, Image and Vision Computing Volume 28, Issue 1, January 2010.

      [2] Fadi Abu-Amara and Ikhlas Abdel-Qader, “HybridMammogram Classification Using Rough Set and Fuzzy Classifierâ€, International Journal of Biomedical Imaging, Volume 2009, July 2009.

      [3] B. Surendiran and A. Vadivel, “ A hybrid classifier for mammogram mass classification using various new geometric shape and margin featuresâ€, Int. J. Rapid Manufacturing, Vol. 2, Nos. 1/2, pp.56–75, 2011.

      [4] Amal AlQoud and M. Arfan Jaffar, “Hybrid Gabor based Local Binary Patterns Texture Features for classification of Breast Mammogramsâ€, International Journal of Computer Science and Network Security, VOL.16 No. 2, February 2016.

      [5] M. Arfan Jaffar, “Hybrid Texture based Classification of Breast Mammograms using Adaboost Classifierâ€, International Journal of Advanced Computer Science and Applications, Vol. 8, No. 5, 2017.

      [6] R.Nithya, B.Santhi, “Mammogram Classification Using Maximum Difference Feature Selection Methodâ€, Journal of Theoretical and Applied Information Technology, Vol. 33 No.2, November 2011.

      [7] Ping Zhang, Kuldeep Kumar, and Brijesh Verma, “A Hybrid Classifier for Mass Classification with Different Kinds of Features in Mammographyâ€, FSKD, LNAI 3614, pp. 316 – 319, 2005.

      [8] Ankita Satyendra Singh, M. M. Pawar, “Mass Classification of Mammogram Images using Selected Textural Features with PNN Classifier†International Journal of Innovative Research in Computer and Communication Engineering, Vol.5, Special Issue 4, June 2017.

      [9] Jinchang Ren, DongWang, JianminJiang , “Effective recognition of MCCs in mammograms using an improved neural classifier†Engineering Applications of Artificial Intelligence 24 ,638–645,2011.

      [10] R. Swiniarski Hun Ki Lim, “Independent Component Analysis, Principal Component Analysis and Rough Sets in Hybrid Mammogram Classificationâ€, International SPIE Symposium Medical Imaging, 2003.

      [11] S Murali and P Sathees Kumar, “Multiclass classification of mammogram images with GLCM featuresâ€, International Journal of Engineering Research Science and Technology, Vol. 4, No. 1, February 2015

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  • How to Cite

    Jayandhi, G., R.Dhaya, D., & R.Kanthavel, D. (2018). Multi Intelligent Fuzzy Integration (Mifi) Support Vector Machines for the Mammogram Classification. International Journal of Engineering & Technology, 7(3.34), 251-255. https://doi.org/10.14419/ijet.v7i3.34.18978

    Received date: 2018-09-04

    Accepted date: 2018-09-04

    Published date: 2018-09-01