  In the existing research of mammogram image classification, either clinical
data or image features of a specific type is considered along with the
supervised classifiers such as Neural Network (NN) and Support Vector Machine
(SVM). This paper considers automated classification of breast tissue type as
benign or malignant using Weighted Feature Support Vector Machine (WFSVM)
through constructing the precomputed kernel function by assigning more weight
to relevant features using the principle of maximizing deviations. Initially,
MIAS dataset of mammogram images is divided into training and test set, then
the preprocessing techniques such as noise removal and background removal are
applied to the input images and the Region of Interest (ROI) is identified. The
statistical features and texture features are extracted from the ROI and the
clinical features are obtained directly from the dataset. The extracted
features of the training dataset are used to construct the weighted features
and precomputed linear kernel for training the WFSVM, from which the training
model file is created. Using this model file the kernel matrix of test samples
is classified as benign or malignant. This analysis shows that the texture
features have resulted in better accuracy than the other features with WFSVM
and SVM. However, the number of support vectors created in WFSVM is less than
the SVM classifier.
