CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Optimization of Artificial Neural Network Breast Cancer Detection System based on Image Registration Techniques

by Satish Saini, Ritu Vijay
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 14
Year of Publication: 2014
Authors: Satish Saini, Ritu Vijay
10.5120/18447-9837

Satish Saini, Ritu Vijay . Optimization of Artificial Neural Network Breast Cancer Detection System based on Image Registration Techniques. International Journal of Computer Applications. 105, 14 ( November 2014), 26-29. DOI=10.5120/18447-9837

@article{ 10.5120/18447-9837,
author = { Satish Saini, Ritu Vijay },
title = { Optimization of Artificial Neural Network Breast Cancer Detection System based on Image Registration Techniques },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 14 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 26-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number14/18447-9837/ },
doi = { 10.5120/18447-9837 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:43.173709+05:30
%A Satish Saini
%A Ritu Vijay
%T Optimization of Artificial Neural Network Breast Cancer Detection System based on Image Registration Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 14
%P 26-29
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper presents a Feed-forward back-propagation Artificial Neural Network (ANN) model for detection of breast cancer using Image Registration Techniques. Gray Level Co-occurrence Matrix (GLCM) features extracted from the known mammogram images are used to train the ANN based detection system. The ANN based detection system will be investigated for different number of neurons and layers on the basis of Mean Square Error (MSE) and optimum number of neurons and layers will be chosen.

References
  1. http://www. cancer. org. American Cancer Society, 2012.
  2. Fred S. Azar, "Imaging Techniques for Detecting Breast Cancer: Survey and Perspectives", Technical Reports (CIS), University of Pennsylvania, 2000, pp. 1-6.
  3. Wang T. , Karayiannis N. , "Detection of microcalcifications in digital mammograms using wavelets", IEEE Transactions on Medical Imaging, 1998, vol. 17. 4, pp. 498–509.
  4. Manjusha P. Deshmukh & Udhav Bhosle, "A survey of image registration", International Journal of Image Processing (IJIP), 2011, vol. 5. 3, 1-6.
  5. Keivanfard, F. , Teshnehlab, M. , Shoorehdeli, M. A. , Ke Nie, Min-Ying Su. "Feature selection and classification of breast cancer on dynamic Magnetic Resonance Imaging by using artificial neural networks", 17th Iranian Conference of Biomedical Engineering, 3-4 Nov. 2010. 1-4.
  6. Dheeba J. , Tamil Selv S. , "A CAD System for Breast Cancer Diagnosis Using Modified Genetic Algorithm Optimized Artificial Neural Network", SEMCCO (Springer). Part I. LNCS 7076, 2011, 349–357.
  7. Salim MI, Ahmad AH, Arffin I, "Developmemnt of Breast cancer detection tool using Hybrid Magnetoacoustic method and Artificial Neural Network", International Journal of Biomedical Engineering, 2012, vol. 6, 61-68.
  8. Ahmad Taher Azhar, Ahmed Shaimaa, El-Said, "Probabilistic Neural Networks for Breast cancer Classification", Neural Computing and Applications, Springer, 2013, vol. 23, 1737-1751.
  9. Dheeba J. , Albert Singh N. , Tamil Selvi S. , "Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach", Journal of Biomedical Informatics, Elsevier, 2014, vol. 49, 45–52.
  10. Senapati M. R. , Panda G. , Dash Hybrid P. K. , "Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection", Neural Computing & Applications, Springer, 2014, vol. 24,745–753.
  11. Gonzalez R. C. , Woods R. E. , Richard E, Steven L. E. s, " Digital Image Processing" ,Second Edition, Pearson Education, New Delhi,India.
  12. Devendram V. , Thiagarajan H. , " Texture based scene categorization using Arti?cial Neural Networks and Support Vector Machines: a comparative study", ICGST-GVIP, 2008, vol. 8, 45-52.
  13. Saini S. , Vijay R. , "Performance Analysis of Artificial Neural Network Based Breast Cancer Detection System", International Journal of Soft Computing and Engineering, 2014, vol. 4,Issue 470-72
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network Image Registration Techniques Mammogram.

Powered by PhDFocusTM