Mammogram Classification Using Gray-Level Co-occurrence Matrix for Diagnosis of Breast Cancer

Abstract

Breast cancer is one of the most common forms of cancer in women worldwide. Most cases of breast cancer can be prevented through screening programs aimed at detecting abnormal tissue. So, early detection and diagnosis is the best way to cure breast cancer to decrease the mortality rate. Computer Aided Diagnosis (CAD) system provides an alternative tool to the radiologist for the screening and diagnosis of breast cancer. In this paper, an automated CAD system is proposed to classify the breast tissues as normal or abnormal. Artifacts are removed using ROI extraction process and noise has been removed by the 2D median filter. Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm is used to improve the appearance of the image. The texture features are extracted using Gray Level Co-occurrence Matrix (GLCM) of the region of interest (ROI) of a mammogram. The standard Mammographic Image Analysis Society (MIAS) database images are considered for the evaluation. K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used as classifiers. For each classifier, the performance factor such as sensitivity, specificity and accuracy are computed. It is observed that the proposed scheme with 3NN classifier outperforms SVM and ANN by giving 95% accuracy, 100% sensitivity and 90% specificity to classify mammogram images as normal or abnormal.