AUTOMATED IDENTIFICATION OF BREAST CANCER USING HIGHER ORDER SPECTRA

Abstract

"Breast cancer is the second leading cause of death in women. It occurs when cells in the breast begin to grow out of control and invade nearby tissues or spread throughout the body.

This project proposes of a comparative approach for classification of the three kinds of mammograms, normal, benign and cancer. The features are extracted from the raw images using the image processing techniques and fed to the two classifiers, the support vector machine (SVM) and the Gaussian mixture model (GMM), for comparison.

The aim of this study is to, develop a feasible interpretive software system which will be able to detect and classify breast cancer patients by employing Higher Order Spectra (HOS) and data mining techniques. The main approach of this project is to employ non-linear features of the HOS to detect and classify breast cancer patients.

HOS is known to be efficient as it is more suitable for the detection of shapes. The aim of using HOS is to automatically identify and classify the three kinds of mammograms.

The project protocol uses 205 subjects, consisting of 80 normal, 75 benign and 50 cancer, breast conditions."