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

Feature extraction plays a vital role in image processing techniques for medical imaging. In this paper, the researchers proposeda breast cancer classification system by using image processing techniques to help the radiologists that the system can improvethe mammogram screening process and increase the life of cancer patients. Our breast cancer classification system based on thecombination of first-order Statistics features and second-order Gray Level Co-occurrence Matrix (GLCM) features and SupportVector Machine is used as a classifier. This system is composed of five stages. At first, preprocessing is carried out for removingnoise and detail artifacts from an image, reducing the size of the image by cropping, enhancing the image to show clearly theappearance of the image. Median Filters are used to remove the artifact, noise, high-frequency components and unwanted partsin the background of the mammogram images. Secondly, Otsu segmentation is used to extract the breast region from thebackground 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, performancecomparison of the first order, second order features and combination of first-order statistics and second-order GLCM features forbreast cancer detection system are done with classification accuracy scores. In this system, an input MIAS database are used forour breast cancer detection system.

• Publication year

  2020

• Venue

  International Journal of Advances in Scientific Research and Engineering

• Publication date

  2020-03-06

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.31695/ijasre.2020.33753
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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