Mammogram Classification using Law's Texture Energy Measure and Neural Networks

Mammography is the best approach in early detection of breast cancer. In mammography classification, accuracy is determined by feature extraction methods and classifier. In this study, we propose a mammogram classification using Law's Texture Energy Measure (LAWS) as texture feature extraction method. Artificial Neural Network (ANN) is used as classifier for normal- abnormal and benign-malignant images. Training data for the mammogram classification model is retrieved from MIAS database. Result shows that LAWS provides better accuracy than other similar method such as GLCM. LAWS provide93.90% accuracy for normal-abnormal and 83.30% for benign-malignant classification, while GLCM only provides 72.20% accuracy for normal-abnormal and 53.06% for benign-malignant classification.