Computer Aided Diagnosis System for Breast Cancer using ID3 and SVM Based on Slantlet Transform
DOI:
https://doi.org/10.25212/lfu.qzj.2.2.16Keywords:
Breast cancer, CAD, Slantlet, ID3, SVMAbstract
Lately, the woman's chest cancer is the 2nd reason of deaths in females. Mam.mog.ram images are medical images, which can be read by physicians to detect breast carcinomas. In this paper, proposed Computer Aided Diagnosis system that can assist the doctors in hospitals to improve the diagnosis of the disease to detect cancer cells. Enhance the undesirable effects the of mam.mog.ram images by using slantlet transformer , set of different stages and classifies as normal, abnormal according to ID3 and SVM. For the same testing set, the practical outcomes displays SVM classifier with an accuracy of 95% and ID3 classifier with an accuracy of 92% based on MIAS database.
Downloads
References
Smitha P., Shaji L. and Mini M., "A Review of Medical Image Classification Techniques", International Conference on VLSI, Communication and Instrumentation (ICVCI), Karunagapally, 2011.
Sara D. and Abbasi M., " Br.east Cancer Diagnosis System Based on Contourlet Analysis and Support Vector Machine", Department of Computer, Science and Research Branch, Islamic Azad University, Khuzestan, Iran, 2011.
Yajie S., "Normal Mammogram Analysis", PhD Thesis, Department of Computer Science, University of Purdue, 2004.
Monika S., "Computer Aided Diagnosis in Digital Mammography: Classification of Mass and Normal Tissue", MSc Thesis, Department of Computer Science and Engineering, University of South Florida, 2003.
Subbiah B.,"Contourlet Based Texture Analysis and Classification Of Mammogram Images", International Journal of Engineering Science & Technology, Vol. 5 Issue 6, p1228, 2013.
Leena J., Baskaran S. and Govardhan A.," A Robust Approach to Classify Micro calcification In Digital Mammograms Using Contourlet Transform and Support Vector Machine", JCA (ISSN: 0974-1925), Volume VI, Issue 1,2013.
Matheel E, "Color Image Denoising Using Discrete Multiwavelet Transform" , PhD Thesis, Department of Computer Science and Information Systems of the University of technology, 2000.
Panrong X., "Image Compression by Wavelet Transform ", the faculty of the Department of Computer and Information Sciences, East Tennessee State University, Masters Abstracts International, Volume: 40-01, page 0197, 2001
Jing Y., Yong H. and Phooi Y. ,"Recent Trends in Texture Classification: A Review", Symposium on Progress in Information & Communication Technology, pp. 63-68, 2009.
Ivan W. Selesnick, "The Slantlet Transform", IEEE Transactions on Signal Processing, Vol. 47, No. 5, May, 1999..
Iman M. Ga’fer and Afrah L. Mohammed, "Slantlet Transform based Video Denoising", Baghdad Science Journal, Vol. 8, No.2, 2011.
N. B Patil, V. M. Viswanatha and S. Pande, "Slant Transformation as a Tool for PreProcessing in Image Processing", International Journal of Scientific & Engineering Research, Vol. 2, No. 4, April, 2011
Matthew N. and Sajjan G.," Comparative Analysis of Serial Decision Tree Classification Algorithms ", IJCSS, 2013.
Lavanya V. and Rani U.,"Performance Evaluation of Decision Tree Classifiers on Medical Datasets", IJCA, 2011.
C. Chen, C. Chen and C. Chen, “A Comparison of Texture Features Based on SVM and SOM,” ICPR, vol. 2, pp. 630-633, 2006.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 Mohammed Salih Mahdi
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Qalaai Zanist Journal allows the author to retain the copyright in their articles. Articles are instead made available under a Creative Commons license to allow others to freely access, copy and use research provided the author is correctly attributed.
Creative Commons is a licensing scheme that allows authors to license their work so that others may re-use it without having to contact them for permission