International Journal of Advances in Scientific Research and Engineering-IJASRE

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

Article Category: Computer Science and Information Engineering

DOI: 10.31695/IJASRE.2020.33753

Pages: 95-102

Author: Than Than Htay,Su Su Maung,Khine Thin Zar

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.

Keyword: Breast Cancer, Enhancement, Feature Extraction, GLCM, MIAS Database.

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