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Reseach Article

Breast Cancer-Early Detection and Classification Techniques: A Survey

by Anu Appukuttan, Sindhu L.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 11
Year of Publication: 2015
Authors: Anu Appukuttan, Sindhu L.
10.5120/ijca2015907557

Anu Appukuttan, Sindhu L. . Breast Cancer-Early Detection and Classification Techniques: A Survey. International Journal of Computer Applications. 132, 11 ( December 2015), 9-13. DOI=10.5120/ijca2015907557

@article{ 10.5120/ijca2015907557,
author = { Anu Appukuttan, Sindhu L. },
title = { Breast Cancer-Early Detection and Classification Techniques: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 11 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number11/23636-2015907557/ },
doi = { 10.5120/ijca2015907557 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:29:04.868374+05:30
%A Anu Appukuttan
%A Sindhu L.
%T Breast Cancer-Early Detection and Classification Techniques: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 11
%P 9-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast Cancer is the most common incursive cancer which is found in females all through the world. Of all the female cancers it comprises of 16% and it accounts for 22.9% of invasive cancer in women. of all the cancer deaths 18.2% are from breast cancer which includes males and females.. As the modern science is improving many researches and techniques have been emerged to eradicate this dreadful disease. So there is a need of an automated computer aided diagnosis system and it is proposed here. This survey paper focus on highlighting different techniques on enhancement, detection and classification of breast cancer along with its accuracy.

References
  1. Every Women Counts, Resource for Health Professionals.
  2. National Breast Cancer accounts.
  3. A Review On Breast Abnormality Segmentation And Classification Techniques
  4. Huai Li, K. J. Ray Liu and Shih-Chung B. Lo,’Fractal Modeling and Segmentation for the Enhancement of Microcalcifications in Digital Mammograms’, IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 16, no. 6, December 1997.
  5. Vijaya Kumar Gunturu, Ambalika Sharma ‘Contrast Enhancement of Mammographic Images Using Wavelet Transform’,©2010 IEEE.
  6. Ojo J. A., Adepoju T. M., Omdiora E. O., Olabiyisi O. S. and Bello O. T, ‘Pre-Processing Method for Extraction of Pectoral Muscle and Removal of Artefacts in Mammogram,’ IOSR Journal of Computer Engineering (IOSR-JCE) e-Volume 16, Issue 3, Ver. V (May-Jun. 2014).
  7. B. Senthilkumar and G.Umamaheswari, ‘Combination of Novel Enhancement Technique and Fuzzy C Means ClusteringTechnique in Breast Cancer Detection’ Biomedical Research 2013; 24 (2); 252-256.
  8. J. Dheeba, N. Albert Singh, S. Tamil Selvi ‘Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach’, Journal of Biomedical Informatics 49 (2014) 45–52, 2014 Elsevier Inc.
  9. Mohamed Meselhy Eltoukhy, Ibrahima Faye1, Brahim Belhaouari Samir, ‘Breast cancer diagnosis in digital mammogram using multiscale curvelet transform’, Computerized Medical Imaging and Graphics 34 (2010) 269–276.
  10. S.Julian Savari Antony ,Dr.S.Ravi, ‘A New Approach to Determine the Classification of Mammographic Image Using K-Means Clustering Algorithm’, International Journal of Advancements in Research & Technology, Volume 4, Issue 2, February -2015 .
  11. K.Subashini, K.Jeyanthi, ‘Masses detection and classification in ultrasound images’, IOSR Journal of Pharmacy and Biological Sciences (IOSR-JPBS),Volume 9, Issue 3 Ver. II (May -Jun. 2014), PP 48-51
  12. Y.Ireaneus Anna Rejani, Dr.S.Thamarai Selvi Noorul ‘Early Detection Of Breast Cancer Using SVM Classifier Technique’, International Journal on Computer Science and Engineering Vol.1(3), 2009, 127-130
  13. S. Shanthi, and V. Murali Bhaskaran ,’Computer Aided System for Detection and Classification of Breast Cancer’, International Journal of Information Technology, Control and Automation (IJITCA) Vol.2, No.4, October 2012
  14. Neeta Jog, Arvind Pandey, ’Implementation of Segmentation and Classification Techniques for Mammogram Images’, IOSR Journal of Engineering (IOSRJEN), Vol. 05, Issue 02 (February. 2015)
  15. S. Deepa, Dr.V.Subbiah Bharathi, ‘Textural Feature Extraction and Classification of Mammogram Images using CCCM and PNN’, IOSR Journal of Computer Engineering (IOSR-JCE) ,Volume 10, Issue 6 (May. - Jun. 2013)
Index Terms

Computer Science
Information Sciences

Keywords

Accuracy Breast Cancer CAD Classifiers Detection Enhancement. MIAS.