Абстрактный

An improved CAD system for abnormal mammogram image classification using SVM with linear kernel

Anto Sahaya Dhas, Vijikala V

In recent years, breast cancer is a life threatening disease among women. A lot of research had been carried out in the detection and classification of mammogram images to support the physicians. A detailed classification of abnormal mammogram images using support vector machine along with linear kernel is proposed in this paper. The test mammogram images were denoised using Oriented Rician Noise Reduction Anisotropic Diffusion (ORNRAD) filter. The denoised images were segmented by adopting K-means clustering algorithm. Gray-tone Spatial Dependence Matrix (GSDM) and Tamura method is adopted to extract the texture and Tamura features from the segmented image. Genetic algorithm along with Joint entropy is used to select the relevant features. The classification of abnormality was achieved using Support Vector Machine (SVM) along with linear kernel which gives a global classification accuracy of 98.1% is obtained using support vector machine with linear kernel.