## 1 Introduction
A computer-aided diagnostic (CAD) model is used to assist radiologists detect malignant breast cancer tumors [23; 24]. Recently, some research based on deep learning methods has addressed the concept of medical imaging on automated CAD systems [8; 25; 26; 27; 28; 29; 30]. In fact, deep learning approaches are the best option for medical image recognition and classification. A CAD system based on deep learning requires three successive phases: the pre-processing, parameters initialization, extraction of deep features and diagnosis [8; 25]. Deep learning methods can be utilized to directly derive the large low-high levels deep hierarchical feature maps from the identical source breast image (mammogram) [8]. This shows that deep learning is the most efficient and dependable medical imaging method [8; 25; 31]. Recently, several CAD systems based on deep learning have been introduced to detect the breast lesions and are superior to traditional systems [32; 33]. Automated detection of the breast lesions is the main task for the automatic evolution of CAD approaches to improve the diagnosis of breast cancer [8]. Accurate diagnosis of skeptical breast lesions plays an important part in obtaining a high true positive rate and enhancing the breast cancer final diagnosis [8; 26]. Detecting breast lesions is a difficult task because these lesions are so diverse in shape, texture, size, and position. The use of image processing and deep learning methods to detect the breast lesions using traditional methods are proposed in [11; 26]. Traditional technology relies on low-level manual functions and does not yet have the ability to perform identification tasks automatically [25]. Low contrast of mammogram images and detecting asymptomatic breast cancer through mammography is a difficult image classification task from the view of machine learning (ML) because the tumor itself only makes up a small part of the complete breast image. Recently, numerous new approaches based on deep learning recognition have been introduced to solve the problem of poor diagnosis performance of CAD approaches [8; 31]. Classification of breast lesions is the last phase in the CAD model. The aim of this stage is to identify breast lesions that are considered benign or malignant [25; 34]. The importance of the method of classification of breast lesions leans on the validity of the hypothetical characteristics which indicate the  
Figure 1: The breast mammogram of a patient [21]. (**a,b**) Right and left medial-lateral oblique (MLO) view, (**c,d**) Right and left cranio-caudal (CC) view. leading characteristics [8]. Deep features must have an appropriate feature distribution in order to identify the malignant and benign breast lesions [8; 9]. The existing breast cancer detection system is time consuming, expensive, and requires additional work to operate the equipment for radiologists. From an image processing perspective, accurate and automatic detection of breast cancer is not easy. Hence, it is deemed a requisite to diagnose breast cancer at an early stage and treated properly. However, breast cancer detection requires proper screening system and automation because of the following reasons [5]:  
1. False diagnosis and prediction. 2. The tumor appears in an area with significantly lower contrast. 3. Lack of reliability of human experience in diagnosis. 4. Overload of the radiologists. 5. High complexity of memory. 6. Degree of human inaccuracy in diagnosis. 7. Current deep learning algorithms need large amounts of training data to overcome the problems of overfitting. 8. Existing breast cancer detection methods are computationally complex and require more treatment time to identify accurate tumors. In addition, manual breast cancer diagnosis can take months and the localized stage of cancer infection can progress to a critical stage where survival cannot be achieved. Therefore, an accurate automated breast cancer detection method is required to diagnose cancer [35].