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

Abstract EN

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.