Localization of Tumor and its Stage using Intelligent Techniques

Abstract

Breast cancer is the most common disease, which is the leading cause of cancer deaths among women. This deadly disease is curable, if identified at the initial stage. In this paper, we propose a scheme for predicting the real stage of breast cancer by retrieving the mammogram images from the past cases that are similar to the query image. First, Gabor and energy features are extracted to differentiate abnormal tissue textures from normal tissue texture. In the second step, back propagation neural network (BPNN) algorithm is used to detect the tumor. Next, pattern is extracted from the query image and the real stage of breast cancer is identified using the depth of the tumor. In this paper, Euclidean distance metric and Mahalanobis distance metric are used to compute the pattern similarity between the images for retrieval. In the same way, pattern base images also retrieved. The tumor found in these retrieved images shows the same stage of breast cancer related to the query image. Performance of Euclidean and Mahalanobis distance metric is compared using precision recall measures. The proposed approach achieved 87% classification rate.