CFP last date
20 August 2024
Call for Paper
September Edition
IJCA solicits high quality original research papers for the upcoming September edition of the journal. The last date of research paper submission is 20 August 2024

Submit your paper
Know more
Reseach Article

Breast Cancer Diagnosis by CAD

by Nidhal K. El Abbadi, Elaf J. Al Taee
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 5
Year of Publication: 2014
Authors: Nidhal K. El Abbadi, Elaf J. Al Taee
10.5120/17523-8088

Nidhal K. El Abbadi, Elaf J. Al Taee . Breast Cancer Diagnosis by CAD. International Journal of Computer Applications. 100, 5 ( August 2014), 25-29. DOI=10.5120/17523-8088

@article{ 10.5120/17523-8088,
author = { Nidhal K. El Abbadi, Elaf J. Al Taee },
title = { Breast Cancer Diagnosis by CAD },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 5 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number5/17523-8088/ },
doi = { 10.5120/17523-8088 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:29:11.946996+05:30
%A Nidhal K. El Abbadi
%A Elaf J. Al Taee
%T Breast Cancer Diagnosis by CAD
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 5
%P 25-29
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death of female worldwide. Mammogram is one of the most excellent technologies currently being used for diagnosing breast cancer. Computer aided diagnosis helps the radiologists to detect abnormalities earlier than traditional procedures. In this paper, we suggested to use some of features selected to distinguish the benign and malignant breast cancer. Tumor segmented and denoising prior to classification. The accuracy of proposed system was 100%.

References
  1. Polat K. , and Gne S. 2006 Breast Cancer Diagnosis Using Least Square Support Vector Machine, Elsevier, DOI: 10. 1016/j. dsp. 2006. 10. 008.
  2. Cheng, H. D. , Cai, X. , Chen, X. W. , Hu, L. , and Lou, X. , 2003 Computer Aided Detection and Classification of Micro Calcifications in Mammograms: A Survey, Pattern Recognition, vol. 36, pp: 2967–2991.
  3. Sivakumar R. , and Karnan M. 2012 Diagnose Breast Cancer through Mammograms Using EABCO Algorithm, International Journal of Engineering and Technology (IJET), Vol 4, No 5.
  4. Suzuki K. , 2012 A review of computer-aided diagnosis in thoracic and colonic imaging, AME, DOI: 10. 3978/j. issn. 2223-4292. 2012. 09. 02.
  5. Bhagwati Charan Patel, and G. R. Sinha, 2010 An Adaptive K-means Clustering Algorithm for Breast Image Segmentation, International Journal of Computer Applications, Volume 10, No 4. DOI: 10. 5120/1467-1982
  6. Dromain, C. , Boyer, B. , Ferré, R. , Canale, S. , Delaloge, S. , Balleyguier, C. , 2012 Computer Aided Diagnosis (CAD) in the Detection of Breast Cancer, European Journal of Radiology 82, pp 417– 423. Epud 2012, DOI: 10. 1016/j. ejrad. 2012. 03. 005.
  7. Ramani, R. Suthanthira, N. Vanitha, 2014 Computer Aided Detection of Tumours in Mammograms, International Journal of Image Graphics and Signal Processing, 4, 54-59, DOI: 10. 5815/ijigsp. 2014. 04. 07
  8. Amjath Ali J. and J. Janet, 2013 Mass Classification in Digital Mammograms Based on Discrete Shearlet Transform, Journal of Computer Science 9 (6): 726-732.
Index Terms

Computer Science
Information Sciences

Keywords

Breast Cancer Mammography Denoising Diagnosis Image Features.