Analysis on Results Comparison of Feature Extraction Methods for Breast Cancer Classification

Abstract

Feature extraction plays a vital role in image processing techniques for medical imaging. In this paper, the researchers proposed
a breast cancer classification system by using image processing techniques to help the radiologists that the system can improve
the mammogram screening process and increase the life of cancer patients. Our breast cancer classification system based on the
combination of first-order Statistics features and second-order Gray Level Co-occurrence Matrix (GLCM) features and Support
Vector Machine is used as a classifier. This system is composed of five stages. At first, preprocessing is carried out for removing
noise and detail artifacts from an image, reducing the size of the image by cropping, enhancing the image to show clearly the
appearance of the image. Median Filters are used to remove the artifact, noise, high-frequency components and unwanted parts
in the background of the mammogram images. Secondly, Otsu segmentation is used to extract the breast region from the
background image. In a third stage, enhancement are applied on segmented result images to get efficient features for a higher classification accuracy rate. First-order statistics and second-order texture GLCM features are extracted form enhanced image.
Support Vector Machine is used as a classifier for the classification of abnormal and normal images. Finally, performance
comparison of the first order, second order features and combination of first-order statistics and second-order GLCM features for
breast cancer detection system are done with classification accuracy scores. In this system, an input MIAS database are used for
our breast cancer detection system.