Approaches For Automated Detection And Classification Of Masses In Mammograms

Abstract

Breast cancer is one of the most common cancer among women around the world. Several techniques are available for detection of breast cancer. Mammography is one of the most effective tools for early detection. The goal of this research is to increase the diagnostic accuracy of image processing and machine learning techniques for optimum classification between normal and abnormalities in digital mammograms. GLCM texture feature extractions are known to be the most common and powerful techniques for texture analysis. This paper presents an evaluation and comparison of the performance of two different classification methods used to classify the normal and abnormal patterns. The experimental result suggest that Artificial Neural Network is outperformed the other  method.