Affiliations: [a] Department of Computer Engineering, Faculty of Technical Sciences, University of Kragujevac, Cacak, Serbia | [b] Department of Computer Science, Faculty of Electronic Engineering, University of Nis, Nis, Serbia
Correspondence:
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Corresponding author: Marina Milosevic, Department of Computer Engineering, Faculty of Technical Sciences, University of Kragujevac, Svetog Save 65, Cacak 32000, Serbia. E-mail: [email protected].
Abstract: BACKGROUND: Mammography is considered the gold standard for early breast cancer detection but it is very difficult to interpret mammograms for many reason. Computer aided diagnosis (CAD) is an important development that may help to improve the performance in breast cancer detection. OBJECTIVE: We present a CAD system based on feature extraction techniques for detecting abnormal patterns in digital mammograms. METHODS: Computed features based on gray-level co-occurrence matrices (GLCM) are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 texture features are extracted from each mammogram. The ability of feature set in differentiating normal, benign and malign tissue is investigated using a Support Vector Machine (SVM) classifier, Naive Bayes classifier and K-Nearest Neighbor (k-NN) classifier. The efficiency of classification is provided using cross-validation technique. Support Vector Machine was originally designed for binary classification. We constructed a three-class SVM classifier by combining two binary classifiers and then compared his performance with classifiers intended for multi-class classification. To evaluate the classification performance, confusion matrix and Receiver Operating Characteristic (ROC) analysis were performed. RESULTS: Obtained results indicate that SVM classification results are better than the k-NN and Naive Bayes classification results, with accuracy ratio of 65% according to 51.6% and 38.1%, respectively.The unbalanced classification that occurs in all three classification tests is reason for unsatisfactory accuracy. CONCLUSIONS: Obtained experimental results indicate that the proposed three-class SVM classifier is more suitable for practical use than the other two methods.
