ReliefF-Based Feature Selection for Automatic Tumor Classification of Mammogram Images

Mammography is a powerful manual method to detect breast cancer in its early stage. This paper looks at wavelet based machine vision method to automate breast cancer diagnosis by the use of digital mammogram images and presents method to improve their performance by employing feature selection. Machine vision scheme consists of three steps. The first step is preprocessing and here a three levels decomposition wavelet transform of mammogram images is the first step. The second step is the analysis step which consists of feature extraction and selection. Wavelet transform coefficients are the extracted features. Feature selection is the focus of this paper and we employ ReliefF to reduce the number of features by removing the redundant and retaining the most informative ones to find an optimum set of features among the wavelet coefficients for the third step which is the recognition phase. As feature selection reduces the number of features with attention on retaining the most informative ones this improves the performance of recognition phase. The optimum set of features is the input of recognition phase. This paper uses support vector machine as the classifier of recognition phase to distinguish benign mass and malignant tumor. Experimental results verify the performance of the proposed method.