Development of a computer-aided classification system for cancer detection from digital mammograms

Abstract

Mammogram-breast x-ray is considered the most effective, low cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesion exist, only 15 to 30% of masses referred for surgical biopsy are actually malignant. Computer-Aided Classification system was used to help in diagnosing abnormalities faster than traditional screening program without the drawback attribute to human factors. In this work, an approach is proposed to develop a computer-aided classification system for cancer detection from digital mammograms. The proposed system consists of three major steps. The first step is region of interest (ROI) extraction of 256x256 pixels size. The second step is the feature extraction; we used a set of 88 features and we found that 78 of these feature are capable of differentiating between normal and cancerous breast tissues in order to minimize the classification error. The third step is the classification process; we used the technique of the k-Nearest Neighbor (k-NN) to classify between normal and cancerous tissues. The proposed system was shown to have the large potential for cancer detection from digital mammograms.