Hybrid mammogram classification using rough set and fuzzy classifier

Authors:
Fadi Abu-Amara;Ikhlas Abdel-Qader
Affiliations:
Department of Electrical and Computer Engineering, Western Michigan University, MI;Department of Electrical and Computer Engineering, Western Michigan University, MI
Venue:
Journal of Biomedical Imaging
Year:
2009

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Abstract

We propose a computer aided detection (CAD) system for the detection and classification of suspicious regions inmammographic images. This system combines a dimensionality reduction module (using principal component analysis), a feature extraction module (using independent component analysis), and a feature subset selection module (using rough set model). Rough set model is used to reduce the effect of data inconsistency while a fuzzy classifier is integrated into the system to label subimages into normal or abnormal regions. The experimental results show that this system has an accuracy of 84.03% and a recall percentage of 87.28%.