Mammogram image classification using rough neural network

Abstract

Breast cancer is the second leading cause of cancer deaths in women, and it is the most common type of cancer prevalent among women. Detecting tumor using mammogram is a difficult task because of complexity in the image. This brings the necessity of creating automatic tools to find whether a tumor is present or not. In this paper, rough set theory (RST) is integrated with back-propagation network (BPN) to classify digital mammogram images. Basically, RST is used to handle more uncertain data. Mammogram images are acquired from MIAS database. Artifacts and labels are removed using vertical and horizontal sweeping method. RST has also been used to remove pectoral muscles and segmentation. Features are extracted from the segmented mammogram image using GLCM, GLDM, SRDM, NGLCM, and GLRM. Then, the features are normalized, discretized, and then reduced using RST. After that, the classification is performed using RNN. The experimental results show that the RNN performs better than BPN in terms of classification accuracy.