###### Abstract
Image examinations, such as mammography, magnetic resonance imaging (MRI), and computed tomography (CT) image, are widely used to detect the regions of breast lesions and microcalcification in internal and external right/left breast. MRI is costly, and it requires a contrast agent to enhance the images to improve the diagnostic results. However, this contrast agent's dosage level, such as mild or severe level, may affect the patient [6; 13; 14]. Additionally, MRI may be a promising manner in screening younger women with dense breasts at a higher risk level for developing breast cancer. CT imaging offers clearer and better  
Figure 1: Templates of feature patterns are extracted from suspicious mammographic images with a specific bounding box. (**a**) Template patterns for normal, (**b**) template patterns for benign (B), and (**c**) template patterns for malignant (M). resolution images for hard objects than for softer tissue, and may be an assistive tool for monitoring tumor spread. However, radiation is a major concern in CT imaging. X-ray mammography is a first-line imaging examination used to screen the breast lesions in women who have no symptoms of breast cancer. In mediolateral oblique views of mammography images, the focal, linear, segmental, regional, and multiple regional mass distributions indicate the higher probability of malignancy [15]. Hence, morphological patterns are key features in identifying the malignant and benign masses, as shown in Figure 1, which refers to the morphological descriptors of the Breast Imaging-Reporting and Data System (BI-RADS). The seven assessment categories are used to characterize lesions [11; 16; 17], and the assessment of BI-RADS categories for mammogram classification is shown in Table 1.  
For mammography databases, such as Suspicious Regions on Mammograms from Palermo Polyclinic (343 images, manually annotated by experts) [18], the Digital Database of Screening Mammography (DDSM) (2620 studies from hospitals and medical universities) [19], the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (modified and standardized version of DDSM) [20], Image Retrieval in Medical Applications (11,000 X-ray images, dataset with region of interest (ROI) annotations) [21], and the Mammographic Image Analysis Society (MIAS) (322 screening mammograms) [22; 23], the clinical information was annotated by expert radiologists, including image size, background tissue types, class and severity of abnormality (breast lesions), and coordinates of center of abnormalities. Hence, in an ROI, researchers can easily extract feature patterns (Figure 1) as training datasets to train the supervised ML- and DL-based classifier for mass/suspicious lesion segmentation, mass detection/classification, and abnormality detection, such as a support vector machine (SVM), artificial neural network (ANN), k-nearest neighbor classifier, or deep multilayer convolutional neural network (CNN) [24; 25; 26; 27; 28; 29]. They can be carried out on structured data for binary, multiclass, and multi-label classification applications [30]. The above-mentioned supervised learning methods can be used to train a classifier model with labeled feature patterns for breast lesion detection. However, traditional ML-based methods, which consist of an input layer, one or more hidden layers, and an output layer, lack the feature enhancement and feature extraction functions. High-dimensional data processing is a major concern. The preprocesses of feature selection and feature extraction are used to reduce the data dimensionality for overcoming this drawback.