Hybrid Mammogram Classification Using Rough Set and Fuzzy Classifier | Zendy

Fadi Abu-Amara | Zendy; Ikhlas AbdelQader | Zendy

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Hybrid Mammogram Classification Using Rough Set and Fuzzy Classifier

Author(s) -

Fadi Abu-Amara,

Ikhlas AbdelQader

Publication year - 2009

Publication title -

international journal of biomedical imaging

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.626

H-Index - 41

eISSN - 1687-4196

pISSN - 1687-4188

DOI - 10.1155/2009/680508

Subject(s) - pattern recognition (psychology) , computer science , principal component analysis , classifier (uml) , artificial intelligence , feature selection , rough set , data mining , dimensionality reduction , precision and recall , feature extraction , fuzzy logic

We propose a computer aided detection (CAD) system for the detection and classification of suspicious regions in mammographic 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%.

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