# A Grey Wolf-Based Method for Mammographic Mass Classification  
[PERSON]  
1 Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismaila 41522, Egypt; [EMAIL_ADDRESS] (M.T. ); [EMAIL_ADDRESS]. A mammogram is an X-ray picture of the breast. The tumours have a high attenuation factor for X-rays. One limitation is when the breast tissue consists mainly of dense tissues, as the case with young women, and hence, challenges arise to detect these abnormalities. Therefore, an efficient method/system is still needed to detect and classify cancer from mammograms while reducing false positive and false negative ratios. Such method/system is important as a second reader to help the radiologists [4; 5; 6]. For mammogram analysis and classification, the multi-resolution analysis (MRA) approach [7] has been successfully used [8; 9]. In the literature, [PERSON]. [10; 11; 12] applied multi-resolution techniques to extract a set of different features. The mammographic images have been decomposed into several sub-bands. Curvelet and wavelet, as an efficient MRA, are used to analyse the original mammogram image in order to find out the features. In the literature, some authors selected the biggest coefficients [9], while others selected some statistical features of sub-bands [12]. On the other hand, some authors have combined the MRA features with the co-occurrence matrix features [10]. [PERSON] et al. [13] used a set of features obtained using exact Gaussian-Hermite moments and employed three different classifiers. The successful employment of orthogonal moments encourage the authors to integrate three kinds of orthogonal moments in [14]. [PERSON] [15] used a grey wolf optimization (GWO) algorithm to train feed foreword neural network (FFNN), which is then used for mammogram breast cancer classification. The proposed GWO-FFNN method showed promising results when evaluated using the Wisconsin Hospital Breast Cancer dataset. In addition, [PERSON] et al. [16] have also applied the GWO algorithm to optimize the hidden node parameters of the extreme learning machine aiming to obtain a better performance of breast cancer detection. In another study [17], the GWO was combined with the decision tree to classify gene expression data. This combination was proposed to select a small set of genes from a group of genes to identify cancer. In addition to employing GWO algorithm in breast cancer detection, it has been used as a feature selection technique in other applications in image processing. For example, it is used in the face recognition system [18]. GWO was used for feature selection, where k-Nearest Neighbour (KNN) was used for classification. The proposed method was estimated based on Yale face dataset. The obtained results showed better accuracy and run time compared to related work. In addition, [PERSON] et al. in [19], employed GWO as a feature selection technique for facial emotion recognition systems. They also used it along with neural network (NN) as a classifier for emotions from selected features. Comparing to conventional methods, NN-Levenberg-Marquardt (LM) and NN-particle swarm optimization (PSO), the accuracy of the obtained results was better than that of traditional methods. In this paper, the proposed work intends to employ the BGWO as an efficient feature selection algorithm to select the most discriminative features that can distinguish between different mammographic image classes. The obtained feature vector is introduced to the random forest classifier to distinguish the normal mammographic image from the abnormal images in the first classification step. Secondly, to define the benign from the malignant region. A critical contribution of this work is to reduce the feature vector dimensionality. The computational complexity is scaled down, i.e., the feature vector length has been reduced to a small number of features, that will increase the speed of the extraction and matching processes.