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

Nithya; Bhuvaneswari; Senthil

doi:doi:10.11648/j.ajnna.20190501.13

| Peer-Reviewed

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

Nithya, Bhuvaneswari, Senthil

Published in American Journal of Neural Networks and Applications (Volume 5, Issue 1)

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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.

Published in	American Journal of Neural Networks and Applications (Volume 5, Issue 1)
DOI	10.11648/j.ajnna.20190501.13
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), 2019. Published by Science Publishing Group

Keywords

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

References

[1]	Mayr FB, Yende S, Angus DC, Epidemiology of severe sepsis, Virulence, vol.5(1), pp.4-11(2014).
[2]	Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A, Global cancer statistics, CA: a cancer journal for clinicians, vol.65(2), pp.87-108 (2015).
[3]	George J. Miao, Kathleen H. Miao, Julia H. Miao, “Neural pattern Recognition Model for Breast Cancer Diagnosis” Journal of selected areas in Bioinformatics, August edition, pp.1-8 (2012).
[4]	S. Saheb Basha, Satya Prasad, “Automatic detection of breast cancer mass in mammograms using morphological operators and fuzzy c-means clustering” Journal of theoretical and applied information technology, pp. 704-709.
[5]	Carlos Andres Pena-Reyes, Moshe Sipper, “A fuzzy-genetic approach to breast cancer diagnosis” Artificial Intelligence in Medicine, vol.17, pp. 131-155 (1999).
[6]	Kovalerchuk B, Triantaphyllou E, Ruiz JF, Clayton J. “Fuzzy logic in computer-aided breast cancer diagnosis: analysis of population”, Artificial Intelligence in Medicine, vol.11, pp. 75-85 (1997).
[7]	Asif HM, Sultana S, Akhtar N, Rehman JU, Rehman RU, Prevalence, risk factors and disease knowledge of breast cancer in Pakistan, Asian Pac J Cancer Prev, vol.15(11), pp.4411-6 (2014).
[8]	Steven D, Fitch M, Dhaliwal H, Kirk-Gardner R, Sevean P, Jamieson J, et al., editors. Knowledge, attitudes, beliefs, and practices regarding breast and cervical cancer screening in selected ethnocultural groups in Northwestern Ontario. Oncology nursing forum (2004).
[9]	Coughlin SS, Thompson TD, Hall HI, Logan P, Uhler RJ. Breast and cervical carcinoma screening practices among women in rural and nonrural areas of the United States, 1998–1999. Cancer, vol.94(11), pp.2801-12 (2002).
[10]	Atanassov K. T, Intuitionistic fuzzy set past, present and future, www.eusflat.org/publicaions/proceedings/.2003/./4Atanassov.pdf.
[11]	Zhang H. M, Chen Z. S. Q, On clustering approach to intuitionistic fuzzy sets, control and decision. vol.22, pp.882-888 (2007).
[12]	Chaira T, A novel intuitionistic fuzzyc-means clustering algorithm and its application to medical images, Applied soft computting, vol.11, pp.1711-1717 (2011).
[13]	Prabhjot kaur, Soni A. K et, Novel Intuitionistic fuzzy c-means clustering for Linearly and nonlinearly separable data, Wseas transactions on computers, vol. 11(3), pp. 65-75 (2012).
[14]	Dhirendra kumar, Hanuman verma, et, A modified intuitionistic fuzzy c-means clustering approach to segment human brain MRI image, Multimedia tools and applications, pp. 1-24 (2018).
[15]	Damodar Reddy, Devender Mishra, et, MST-based cluster initialization for k-means, Berlin Springer-verlag, pp.329-338 (2010).
[16]	Lan Huang, Shixian du, Yu Zhang et, K-means initial clustering center optimal algorithm based on kruskal, Journal of information & computational science, vol.9(9), pp.2387-239 (2012).
[17]	Edward W. Packel, Functional Analysis, Intext Educational Publishers, NewYork (1974).
[18]	Xie X. L and Beni G, A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell., Vol. 3(8), pp-841-847(1991).
[19]	Bezdek J. C., Hall L. O., Clarke L. P, Review of MR image segmentation techniques using pattern recognition, medical physics 20(4), pp. 1033-1048 (1993).

Cite This Article

Plain Text BibTeX RIS

APA Style

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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
SP  - 12
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  -

Copy | Download

Author Information

Nithya

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

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

District Rural Development Agency, Dindigul, India

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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
SP  - 12
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  -

Copy | Download