A Computer-Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier. (18th March 2008)

• Record Type: Journal Article
• Authors: Abdel-Qader, Ikhlas; Abu-Amara, Fadi
• Other Names: Pietka Ewa Academic Editor.
• Is Part Of: Modelling and simulation in engineering. Volume 2008(2008)
• Publication Date: 2008-03-18
• Subjects: Engineering -- Simulation methods -- Periodicals; Engineering -- Mathematical models -- Periodicals; 620.004
• DOI: 10.1155/2008/238305
• Languages: English
• ISSNs: 1687-5591

Abstract

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.