دانلود 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.