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Modelling and Simulation in Engineering
Volume 2008, Article ID 238305, 9 pages
Research Article

A Computer-Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

Department of Electrical and Computer Engineering, Western Michigan University, MI 49008, USA

Received 27 August 2007; Revised 10 January 2008; Accepted 11 March 2008

Academic Editor: Ewa Pietka

Copyright © 2008 Ikhlas Abdel-Qader and Fadi Abu-Amara. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Screening mammograms is a repetitive task that causes fatigue and eye strain since for every thousand cases analyzed by a radiologist, only 3–4 are cancerous and thus an abnormality may be overlooked. Computer-aided detection (CAD) algorithms were developed to assist radiologists in detecting mammographic lesions. In this paper, a computer-aided detection and diagnosis (CADD) system for breast cancer is developed. The framework is based on combining principal component analysis (PCA), independent component analysis (ICA), and a fuzzy classifier to identify and label suspicious regions. This is a novel approach since it uses a fuzzy classifier integrated into the ICA model. Implemented and tested using MIAS database. This algorithm results in the classification of a mammogram as either normal or abnormal. Furthermore, if abnormal, it differentiates it into a benign or a malignant tissue. Results show that this system has 84.03% accuracy in detecting all kinds of abnormalities and 78% diagnosis accuracy.