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Experimental Investigation of Classification Algorithms for Predicting Lesion Type on Breast DCE-MR Images

by Janaki Sathya, K. Geetha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 82 - Number 4
Year of Publication: 2013
Authors: Janaki Sathya, K. Geetha
10.5120/14101-2125

Janaki Sathya, K. Geetha . Experimental Investigation of Classification Algorithms for Predicting Lesion Type on Breast DCE-MR Images. International Journal of Computer Applications. 82, 4 ( November 2013), 1-8. DOI=10.5120/14101-2125

@article{ 10.5120/14101-2125,
author = { Janaki Sathya, K. Geetha },
title = { Experimental Investigation of Classification Algorithms for Predicting Lesion Type on Breast DCE-MR Images },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 82 },
number = { 4 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume82/number4/14101-2125/ },
doi = { 10.5120/14101-2125 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:51.746867+05:30
%A Janaki Sathya
%A K. Geetha
%T Experimental Investigation of Classification Algorithms for Predicting Lesion Type on Breast DCE-MR Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 82
%N 4
%P 1-8
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Timely revealing of breast cancer is one of the most important issues in determining prognosis for women with malignant tumors. Dynamic contrast-enhanced (DCE) MRI is being increasingly used in the clinical setting to help detect and characterise tissue, suspicious for malignancy and has been shown to be the most sensitive modality for screening high-risk women. Computer-assisted evaluation (CAE) systems have the potential to assist radiologists in the early detection of cancer. A crucial module of the development of such a CAE system will be the selection of an appropriate classification function responsible for separating malignant and benign lesions. The motivation of this paper is to provide qualitative evaluation of three advanced classifiers like artificial neural network, support vector machine and artificial bee colony optimization algorithm trained neural network are being developed for classification of the suspicious lesions in breast MRI. A comparative study of these techniques for lesion classification is made to identify relative merits. As a result, the paper concluded that the neural network trained by artificial bee colony optimization algorithm based classifier outperforms all other explored classifiers for the examined dataset of breast DCE –MR images.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Dynamic Contrast Enhanced Magnetic Resonance Images (DCE-MRI) Artificial Neural Networks Support Vector Machine Artificial Bee Colony Optimization Statistical Texture Features and Mass Classification.

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