## 2 Related Work  
Feature extraction is crucial in the CAD system, and a variety of methods are introduced to distinguish between malignant and benign masses. Some studies have used shape features for mass classification from mammogram images such as mean intensity, perimeter, diameter, centroid, and area [23]. The neural network (NN) with many hidden nodes and additional features that are fed to the classifier obtained high accuracy for only 40 Mini-MIAS images. A mammogram classification using the Hough transform to assess the parameters of the shape from its boundary points was proposed in [24]. It is used to insulate features of specific shapes in mammogram images. This work has been evaluated on only 95 mammogram images based on the support vector machine (SVM) classifier. Some studies have reviewed the contributions of texture to the risk assessment for each density. Patches of mammogram images consist of directionally oriented ligaments, ducts, texture features of the image due to its fibrolandular tissues, and blood vessels. Patches of texture features based on mammogram images can be classified into statistical [25], local pattern histogram [26], directional [27], and transform-based [28]. Statistical features, such as entropy, mean, variance, kurtosis, contrast, correlation, energy, coarseness, standard deviation, direction, linearity, regularity, skewness, and roughness, can be derived from five sets of statistical texture features, namely GLCM, GLRLM, Tamura features, first-order statistics (FOS), and GLDM. The dynamic cross-propagation algorithm (DCPA) for mammogram classification was introduced in [29]. Adaptive median filter was used for noise reduction, and superior ROI (SROI) was used for ROI segmentation. Some statistical features, such as mean, entropy, standard deviation, variance, correlation, skewness, kurtosis, homogeneity, contrast, and entropy value, were extracted from the ROI and used to discover the loss function in the image micro and then compared with the training model. This work has been tested on DDSM datasets. [PERSON] and [PERSON] [30] utilized an optimized hybrid classifier based on a convolutional neural network (CNN) and a recurrent neural network (RNN). Median filter is used to reduce noise; then, region growing based on a hybrid metaheuristic technique is used for tumor segmentation. The features of GLCM and GRLM are extracted from the segmented tumor. The statistical features extracted are used as input to RNN and the segmented binary image is used as input to CNN. The work obtained higher results than conventional methods when the AND operation of the output of two classifiers yields the overall diagnostic accuracy. The study [31] reinforces the statistical characteristics of GLCM and GLRLM to improve the differentiation of the masses with the help of super-resolution reconstruction. This study obtained an accuracy of 96.7% on the MIAS dataset. The classification of breast density was proposed in [32]. Breast glandular tissue was segmented by performing a new threshold technique. Mean, skewness, and kurtosis features were obtained by implementing the GLCM texture feature. SVM and extreme learning machine (ELM) were used to classify mixed feature vectors. SVM obtained an accuracy of 96.19% and 96.35% for the MIAS and DDSM databases, respectively. Some studies have exploited the local distribution of textural features for mammogram classification, including histogram of gradients and local configure pattern (LCP) [33], local ternary pattern (LTP) [34], and local quinary patterns (LQP) [35]. The study of [36] proposes a local photometric attribute (LPA) based on a local texture feature to classify mammograms into benign and malignant. Local information has been measured through the optical density using LPA; since the region of the background has been suppressed, it can help in providing mass lesion details. The performance of this method has been evaluated based on receiver operating characteristic (ROC) and accuracy.