Independent Component Analysis in Breast Tissues Mammograms Images Classification Using LDA and SVM

Female breast cancer is the major cause of death in western countries. Efforts in computer vision have been made in order to help improving the diagnostic accuracy by radiologists. In this paper, we present a methodology that uses Independent Component Analysis (ICA) along with Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) to distinguish between Mass or Non-Mass and Benign or Malign tissues from mammograms. As a result, we found the following: LDA reaches 89,5% of accuracy to discriminante Mass or Non-Mass and 95,2% to discriminate Benign or Malignant in DDSM database and in MIAS database we obtained 85 % to discriminate Mass or Non-Mass and 88% of to discriminate Benign or Malignant; SVM reaches 99,6% of accuracy to discriminate Mass or Non-Mass and 99,5% to discriminate Benign or Malignat in DDSM database and in MIAS database we obtained 97% to discriminate Mass or Non-Mass and 100% to discriminate Benign or Malignant.