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An Automated Classification of Microcalcification Clusters in Mammograms using Dual Tree M-Band Wavelet Transform and Support Vector Machine

by C. Suba, K. Nirmala
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 20
Year of Publication: 2015
Authors: C. Suba, K. Nirmala
10.5120/20269-2678

C. Suba, K. Nirmala . An Automated Classification of Microcalcification Clusters in Mammograms using Dual Tree M-Band Wavelet Transform and Support Vector Machine. International Journal of Computer Applications. 115, 20 ( April 2015), 24-29. DOI=10.5120/20269-2678

@article{ 10.5120/20269-2678,
author = { C. Suba, K. Nirmala },
title = { An Automated Classification of Microcalcification Clusters in Mammograms using Dual Tree M-Band Wavelet Transform and Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 20 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number20/20269-2678/ },
doi = { 10.5120/20269-2678 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:55:25.077270+05:30
%A C. Suba
%A K. Nirmala
%T An Automated Classification of Microcalcification Clusters in Mammograms using Dual Tree M-Band Wavelet Transform and Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 20
%P 24-29
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is the second leading cause of cancer deaths after lung cancer. In order to avoid mortality due to breast cancer, an efficient computer aided diagnosis system for early prediction of breast cancer is needed. In this paper, an efficient computerized system is designed for the classification of Microcalcification Clusters (MC) in digitized mammograms. The proposed system uses Dual Tree M-Band Wavelet Transform (DTMBWT) to represent the digital mammogram in a multiresolution manner and Support Vector Machine (SVM) for classification. The extracted sub band energies from DTMBWT decomposed mammograms are used as distinguishable features for the classification of MCs into either malignant or benign by SVM classifier. The results show that the proposed DTMBWT based classification system achieves 91. 83%accuracy on Mammographic Image Analysis Society (MIAS) database images.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Digital mammography microcalcification benign malignant wavelet transform support vector machine.

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