Abstract
This paper presents a feature fusion technique that can be used for classification of ROIs in breast cancer into normal and abnormal classes. The texture features are extracted using geometric invariant shift transform and statistical features from the curvelet grey level co-occurrence matrices. First classification accuracy of both methods were evaluated independently. Later, feature fusion is done to improve the classification performance. Support vector machine classifier with polynomial kernel was implemented using 2 × 5 folds cross validation. Fusion of features produces better results with accuracy of 92.39 % as compared to 77.97 % and 91 % for GIST and CGLCM respectively.