A computer-aided diagnosis system for breast cancer using independent component analysis and fuzzy classifier

Authors:
Ikhlas Abdel-Qader;Fadi Abu-Amara
Affiliations:
Department of Electrical and Computer Engineering, Western Michigan University, MI;Department of Electrical and Computer Engineering, Western Michigan University, MI
Venue:
Modelling and Simulation in Engineering - Modelling and simulation: computational intelligence in medicine
Year:
2008

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Abstract

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.