## 4 Discussion  
In this paper, we proposed a CAD system for characterizing and discriminating suspicious areas into benign/malignant as a development of the previous work for the classification of microcalcification clusters in full field digital mammograms [15]. The analysis was performed on 96 ROIs collected from 104 digital mammogram images of the BCDR database. For each ROI, firstly, a set of some textural features was computed on a multi-scale decomposition based on the Haar wavelet transform, obtaining a first set of statistical features (HAAR SF set). Secondly, the GLCM on the sub-images decomposed by Haar transform was calculated to derive a different set of textural features, obtaining a second set of statistical features (GLCM SF set). Therefore, all the features obtained were used to train an RF classifier with an embedded method for the feature selection able to discriminate ROIs. The classification performance was evaluated in terms of accuracy, sensitivity, specificity, and AUC values.  
The model developed on the HAAR and GLCM statistical feature set, previously described, was high performing in the prediction of benign and malignant clustered microcalcifications with the RF classifier. It was observed that using both textural feature sets led to a median AUC value of 97.39%, an accuracy of 92.58%, a sensitivity of 93.15%, and a specificity of 89.18%.  
Table 2 summarizes the results of the proposed method and other state-of-the-art models for the classification into benign and malignant ROIs. For this comparison, we considered works on classification of MC clusters that mainly used textural features [16] or a combination of these feature with other typologies [3; 17; 18; 19; 32]. We considered both works that used the same public database and others that collected digital mammography images; since all the databases collected heterogeneous images, often not coming from the same research centers, according to us, this comparison was justified.  
Moreover, in [33; 34], the authors developed a Convolutional Neural Network (CNN) decision support system, which was an artificial feed-forward neural network with tens or hundreds of layers. Each of these layers learned to detect independently the different features of an image: the algorithm divided each image into topologically compact portions, each of which would be processed by the filters in order to search for specific patterns, and the output of each image was used as the input for the next layer. However, this method tended to require more images for training, as the new network needed numerous examples of the object to understand the variation of the features. Training times were often longer, and the number of combinations of existing network layers could make configuring the network from scratch a very complex operation.  
In our work, we used only textural features. The extraction of tissue texture features performed in an automated manner from ROIs did not require any intervention by radiologists and provided characteristic information about microcalcification clusters that may be helpful to human readers to improve the discrimination of malignant and benign lesions.  
Since these works used different digital databases and classifiers, the comparison was purely qualitative. However, the performance of our model was high performing in terms of accuracy and AUC both with respect to works using the same database and to those using the DDSM database. As highlighted in the table, only [18] showed a higher accuracy, but in this work, additional features were used, such as shape features, which required a segmentation process that did not seem to justify the computational effort in terms of performance.