American Journal of Neural Networks and Applications

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Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection

Received: 10 May 2019    Accepted: 10 June 2019    Published: 29 June 2019
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Abstract

Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as lumps and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for womens quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. In this paper, we have presented a novel approach to identify the presence of breast cancer lumps in mammograms. The proposed algorithm for selecting initial cluster centers on the basis of minimal spanning tree (MST) is presented. MST initialization method for the intuitionistic fuzzy c-means clustering algorithm for clear to identify of abnormalities for mammography images and Breast cancer patients symptoms used to predictive probability calculated by Pearson Chi-Square (χ2) test at 0.05 significance level indicate a highly significant correlation between mammography performance and clinical symptoms of breast cancer. Our findings suggest that mammography is highly efficient and promising technique.

DOI 10.11648/j.ajnna.20190501.13
Published in American Journal of Neural Networks and Applications (Volume 5, Issue 1, June 2019)
Page(s) 12-22
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Breast Cancer, Mammograms, Intuitionistic Fuzzy C-means, Initial Cluster Center, Minimum Spanning Tree, Partition Coefficient, Validation Function

References
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Author Information
  • Department of Mathematics, Mother Teresa Women’s University, Kodaikanal, India

  • Department of Mathematics, Mother Teresa Women’s University, Kodaikanal, India

  • District Rural Development Agency, Dindigul, India

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    Nithya, Bhuvaneswari, Senthil. (2019). Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection. American Journal of Neural Networks and Applications, 5(1), 12-22. https://doi.org/10.11648/j.ajnna.20190501.13

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    ACS Style

    Nithya; Bhuvaneswari; Senthil. Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection. Am. J. Neural Netw. Appl. 2019, 5(1), 12-22. doi: 10.11648/j.ajnna.20190501.13

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    AMA Style

    Nithya, Bhuvaneswari, Senthil. Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection. Am J Neural Netw Appl. 2019;5(1):12-22. doi: 10.11648/j.ajnna.20190501.13

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  • @article{10.11648/j.ajnna.20190501.13,
      author = {Nithya and Bhuvaneswari and Senthil},
      title = {Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection},
      journal = {American Journal of Neural Networks and Applications},
      volume = {5},
      number = {1},
      pages = {12-22},
      doi = {10.11648/j.ajnna.20190501.13},
      url = {https://doi.org/10.11648/j.ajnna.20190501.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajnna.20190501.13},
      abstract = {Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as lumps and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for womens quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. In this paper, we have presented a novel approach to identify the presence of breast cancer lumps in mammograms. The proposed algorithm for selecting initial cluster centers on the basis of minimal spanning tree (MST) is presented. MST initialization method for the intuitionistic fuzzy c-means clustering algorithm for clear to identify of abnormalities for mammography images and Breast cancer patients symptoms used to predictive probability calculated by Pearson Chi-Square (χ2) test at 0.05 significance level indicate a highly significant correlation between mammography performance and clinical symptoms of breast cancer. Our findings suggest that mammography is highly efficient and promising technique.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection
    AU  - Nithya
    AU  - Bhuvaneswari
    AU  - Senthil
    Y1  - 2019/06/29
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajnna.20190501.13
    DO  - 10.11648/j.ajnna.20190501.13
    T2  - American Journal of Neural Networks and Applications
    JF  - American Journal of Neural Networks and Applications
    JO  - American Journal of Neural Networks and Applications
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    EP  - 22
    PB  - Science Publishing Group
    SN  - 2469-7419
    UR  - https://doi.org/10.11648/j.ajnna.20190501.13
    AB  - Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as lumps and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for womens quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. In this paper, we have presented a novel approach to identify the presence of breast cancer lumps in mammograms. The proposed algorithm for selecting initial cluster centers on the basis of minimal spanning tree (MST) is presented. MST initialization method for the intuitionistic fuzzy c-means clustering algorithm for clear to identify of abnormalities for mammography images and Breast cancer patients symptoms used to predictive probability calculated by Pearson Chi-Square (χ2) test at 0.05 significance level indicate a highly significant correlation between mammography performance and clinical symptoms of breast cancer. Our findings suggest that mammography is highly efficient and promising technique.
    VL  - 5
    IS  - 1
    ER  - 

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