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Article:An Automated Mass Classification System in Digital Mammograms using Contourlet Transform and Support Vector Machine
by
J.S.Leena Jasmine,
Dr.S.Baskaran,
Dr.A.Govardhan
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 31 - Number 9 |
| Year of Publication: 2011 |
| Authors: J.S.Leena Jasmine, Dr.S.Baskaran, Dr.A.Govardhan |
10.5120/3857-5375
|
J.S.Leena Jasmine, Dr.S.Baskaran, Dr.A.Govardhan . Article:An Automated Mass Classification System in Digital Mammograms using Contourlet Transform and Support Vector Machine. International Journal of Computer Applications. 31, 9 ( October 2011), 54-61. DOI=10.5120/3857-5375
@article{
10.5120/3857-5375,
author = {
J.S.Leena Jasmine,
Dr.S.Baskaran,
Dr.A.Govardhan
},
title = { Article:An Automated Mass Classification System in Digital Mammograms using Contourlet Transform and Support Vector Machine },
journal = {
International Journal of Computer Applications
},
issue_date = { October 2011 },
volume = { 31 },
number = { 9 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = {
54-61
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume31/number9/3857-5375/
},
doi = { 10.5120/3857-5375 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:17:45.576374+05:30
%A J.S.Leena Jasmine
%A Dr.S.Baskaran
%A Dr.A.Govardhan
%T Article:An Automated Mass Classification System in Digital Mammograms using Contourlet Transform and Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 31
%N 9
%P 54-61
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In this paper, an efficient automated mass classification system for breast cancer in digitized mammograms using NonSubsampled Contourlet Transform (NSCT) and Support Vector Machine (SVM) is presented. The classification of masses is achieved by extracting the mass features from the contourlet coefficients of the image and the outcomes are used as an input to the SVM classifier for classification. The system classifies the mammogram images as normal or abnormal, and the abnormal severity as benign or malignant. The evaluation of the system is carried on using mammography image analysis society (MIAS) database. The experimental result shows that the proposed method provides improved classification rate.
References
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Index Terms
Computer Science
Information Sciences
Keywords