Mammogram Classification using Law's Texture Energy Measure and Neural Networks
Arden Sagiterry Setiawan, Elysia Elysia, Julian Wesley, Yudy Purnama · Procedia Computer Science · 2015
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.