Leveraging Textural Features for Mammogram Classification

Cancer is the body' s tissue cells that continue to grow beyond normal and out of control so that cancer cells push normal cells and cause death in normal cells. One type of cancer is cancer that attacks breast tissue or is called breast cancer. The sooner breast cancer is detected, it will increase the chance the patient will survive. One of the techniques in the early detection of breast cancer is mammography screening. To minimize human error in checking the results of mammography, a CAD system is needed in checking the results of mammography. Therefore, in this research, a system that can classify breast tissue from mammogram into three classes, namely normal, benign, and malignant has been built. The performance of the system reaches F1-Score 74.02%, Recall 76.15% and Precision 74.02%. The system achieves this performance by combining the Uniform Local Binary Pattern and GLCM features and the Random Forest classification method.