RETRACTED ARTICLE: An improved data mining technique for classification and detection of breast cancer from mammograms

Abstract

The high incidence of breast cancer in women has increased significantly in the recent years. Mammogram breast X-ray imaging 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---30% of masses referred for surgical biopsy are actually malignant. Physician experience of detecting breast cancer can be assisted by using some computerized feature extraction and classification algorithms. 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 256 × 256 pixels size. The second step is the feature extraction; we used a set of 26 features, and we found that these features 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 association rule mining to classify between normal and cancerous tissues. The proposed system was shown to have the large potential for cancer detection from digital mammograms.