Mutual information-based SVM-RFE for diagnostic classification of digitized mammograms

Computer aided diagnosis (CADx) systems for digitized mammograms solve the problem of classification between benign and malignant tissues while studies have shown that using only a subset of features generated from the mammograms can yield higher classification accuracy. To this end, we propose a mu...

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Bibliographic Details
Published in:Pattern recognition letters 2009-12, Vol.30 (16), p.1489-1495
Main Authors: Yoon, Sejong, Kim, Saejoon
Format: Article
Language:English
Subjects:
Applied sciences
Biological and medical sciences
CADx
Computerized, statistical medical data processing and models in biomedicine
Correlation
Digital mammography
Exact sciences and technology
Feature selection
Information, signal and communications theory
Medical management aid. Diagnosis aid
Medical sciences
Miscellaneous
Mutual information
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
SVM-RFE
Telecommunications and information theory
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Summary:Computer aided diagnosis (CADx) systems for digitized mammograms solve the problem of classification between benign and malignant tissues while studies have shown that using only a subset of features generated from the mammograms can yield higher classification accuracy. To this end, we propose a mutual information-based Support Vector Machine Recursive Feature Elimination (SVM-RFE) as the classification method with feature selection in this paper. We have conducted extensive experiments on publicly available mammographic data and the obtained results indicate that the proposed method outperforms other SVM and SVM-RFE-based methods.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2009.06.012