An Efficient CBIR Approach for Diagnosing the Stages of Breast Cancer Using KNN Classifier

Abstract

This paper proposes a mammogram image retrieval technique using pattern similarity scheme. Comparing previous and current mammogram images associated with pathologic conditions are used to diagnose the real stage of breast cancer by doctors. Lack of awareness and screening programs causes the breast cancer deaths. Early detection is the best way to reduce the deaths per incident ratio. Mammogram is the best one in the currently used technique for diagnosing breast cancer. In this paper, the retrieval process is divided into four distinct parts that are feature extraction, kNN classification, pattern instantiation and computation of pattern similarity. In feature extraction step, low level texture features like entropy, homogeneity, contrast, energy, correlation and run length matrix features are extracted. These extracted features are classified using K- Nearest Neighbor classifier to differentiate the normal tissue from abnormal one. Each group is considered as patterns. Finally, pattern similarity is estimated for retrieving images based on their similarity with the query image. This scheme is effectively applied to the Content Based Image Retrieval systems to retrieve the images from large databases and identify the real stage of breast cancer. If we find cancer in early stages we can cure it.