Curvelet based texture features for breast cancer classifications

Abstract

One of the sources of death among women is breast cancer. It is well known that Mammogram is the best method for breast cancer detection. Subsequently, there are solid requirements for the improvement of computer aided diagnosis (CAD) systems to assist radiologists in making decision. In this paper, the curvelet changes is proposed to classify the breast cancer. Curvelet refers to multi-level change which have the characteristics of directionality and anisotropy. It splits several characteristic impediments of wavelet to edges of an image. Two component extraction techniques were created associated with curvelet and wavelet coefficients to separate among various classes of breast. Finally, the K-Nearest Neighbor (KNN) classifiers were utilized to decide if the district is unusual or ordinary. The adequacy of the suggested strategies has been implemented with Mammographic Image Analysis Society (MIAS) data images. All the dataset is utilized by the suggested strategies. Then calculations have been applied with both curvelet and wavelet for correlation test were performed. The general outcomes show that curvelet change shows superior compared to the wavelet and the thing that matters is measurably noteworthy.