Call for Paper - September 2020 Edition
IJCA solicits original research papers for the September 2020 Edition. Last date of manuscript submission is August 20, 2020. Read More

Diagnose the Stages of Breast Cancer using SVM

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 38 - Number 11
Year of Publication: 2012
Jini R. Marsilin
Dr. G. Wiselin Jiji

Jini R Marsilin and Dr. Wiselin G Jiji. Article: Diagnose the Stages of Breast Cancer Using SVM. International Journal of Computer Applications 38(11):1-6, January 2012. Full text available. BibTeX

	author = {Jini R. Marsilin and Dr. G. Wiselin Jiji},
	title = {Article: Diagnose the Stages of Breast Cancer Using SVM},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {38},
	number = {11},
	pages = {1-6},
	month = {January},
	note = {Full text available}


This paper presents a pattern similarity scheme for predicting the real stage of breast cancer. This project allowed the development of content based image retrieval (CBIR) systems, capable of retrieving images based on their similarity with the query image and identifies the correct stages of the breast cancer. The proposed scheme involves low level feature extraction from images like shape and texture features. Shape features used in this scheme are Zernike moments and Radial Chebyshev moments. Texture features of contrast, energy and run length matrix features are also used with the shape features. These extracted features are then classified using SVM. The output of the SVM is considered as patterns. The similarity between two patterns is estimated as a function of the similarity of both their structures and the measure components. The proposed scheme can be effectively applied for image retrieval from large databases and also used to determine the correct stage of breast cancer and get the treatment in appropriate time.


  • H. Greenspan and A. T. Pinhas, “Medical image categorization and retrieval for PACS using the GMM-KL framework,” IEEE Trans. Inf. Techol. Biomed. vol. 11, no. 2, pp. 190–202, Mar. 2007.
  • J. Yaoa, I, S. Antani, R. Longb, G. Thoma, and Z. Zhanga, “Automatic medical image annotation and retrieval using SECC,” in Proc. 19th Int.Symp. Comput.-Based Med. Syst. (CBMS), Salt Lake City, UT, Jun. 2006.
  • M. M. Rahman, P. Bhattacharya, and B. C. Desai, “A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback,” IEEE Trans. Inf. Technol Biomed., vol. 11, no. 1, pp. 58–67, Jan. 2007.
  • I. El-Naqa, Y. Yang, N. P. Galatsanos, R. M. Nishikawa, and M. N. Wernick, “A similarity learning approach to content-based image retrieval: Application to digital mammography,” IEEE Trans. Med. Imag., vol. 23, no. 10, pp. 1233–1244, Oct. 2004.
  • M. Emre Celebi and Y. Alp Aslandogan “A Comparative Study of Three Moment-Based Shape Descriptors”
  • Ilaria Bartolini, Paolo Ciaccia, Irene Ntoutsi, Marco Patella, and Yannis Theodoridis “A Unified and Flexible Framework for Comparing Simple and Complex Patterns”
  • Mukundan R. (2004) “A New Class of Rotational Invariants Using Discrete Orthogonal Moments” Proceedings of the 6th IASTED Conference on Signal and Image Processing, pp. 80-84
  • Dan Popescu, Radu Dobrescu, and Maximilian Nicolae, “Texture Classification and Defect Detection by Statistical Features”.
  • Renato 0. Stehling Mario A. Nascimento Alexandre X. Falcgo “An Adaptive and Efficient Clustering-Based Approach for Content-Based Image Retrieval in Image Databases”.