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Feature Extraction and Classification of Tissue Mammograms Based on Grayscale and Gray Level Co-occurrence Matrix

Breast cancer is a major cause of death for women in the world. Breast cancer can be diagnosed by various means of examination, including mammography examination, which indicates abnormalities in the breast. Doctors need other information, such as a biopsy to detect breast cancer further. However removal of some tissue can cause bleeding, hematoma formation, and infection. A pattern recognition system is needed using mammogram images for breast cancer detection to avoid unnecessary biopsies. According to research conducted by experts from Kaiser Permanente in Oakland, California, breast tissue density can be one of the factors that determine whether a woman is at risk for breast cancer or not. Breast tissue density is always associated with cancer risk. The denser the breast, the more vulnerable it is to be attacked by cancer. This paper proposes a technique classification of breast tissue density into Glandular, Dense Glandular, or Fatty Glandular groups. The features used are mean, kurtosis, skewness, contrast, correlation, energy, and homogeneity. The proposed system consists of two main stages, namely (a) Performing feature extraction using Grayscale and Gray Level Co-occurrence Matrix (GLCM); (b) Compile transaction data and build a classification model. The evaluation results using the Tree and Random Forest algorithms are the accuracy rate is 92% (Tree), 95% (Random Forest).