Abstract- Screening and early detection of breast cancer needs an automated system that identifies the breast cancer in the
mammograms as early as possible. Breast Cancer is the most often identified cancer among women and major reason for
increasing mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser
availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. An automated
system that segments the mammogram masses and identifies the defect in the mammograms is proposed. The mammogram
images are pre-processed by using median filter and adaptive histogram equalization. From the mammogram images
features are extracted using Gabor algorithm and also by calculating mean and standard deviation of the image. The
selected features were then classified using Support Vector Machine classifier. The classifier first identifies whether the
input image is normal or abnormal. If the image is identified to be abnormal means the breast masses are segmented from
the preprocessed images using Likelihood binarization algorithm. The segmentation algorithm segments the breast masses
from the image based on the clustered result of group of pixels in the image. Then the mammogram image is classified into
Benign or Malignant based on separate label and the features extracted. Finally the performance of the classifier is
measured by calculating accuracy, sensitivity and specificity.
Keywords— Breast cancer, Mammograms, Median filter, Gabor algorithm, SVM, Likelihood binarization algorithm