## 2 Related Work  
Modern medical procedures actively use mammography images for breast cancer diagnosis [37]. This section systematically reviews the esteem achievement and performance on breast cancer detection and classification. Over the past few years, deep learning methods in the field of image recognition, segmentation, detection, and computer vision have received much attention and interest [8; 28; 38]. In fact, deep learning has been utilized to address the inadequacies of traditional CAD approaches [8]. This is because conventional CAD approaches are based on manually developed functions and have limited diagnostic accuracy [35]. The similarity between benign and malignant lesions and their enormous changes in size, shape, color, and texture have challenged traditional methods [19; 25]. CAD approaches have demonstrated the ability to use deep learning to discover complex hierarchical deep features automatically to improve diagnostic operation [8; 32; 35]. Many CAD approaches use conventional image processing and deep learning methods, but the use of deep learning detection and classification in breast imaging is inadequate for breast lesion diagnosis. In recent years, AlexNet [39], ResNet [40], VGG16 [41], Inception [42], GoogleNet [43], DenseNet [44] deep learning models provide improve performance of classification as compared to the shallower ones. The overall classification accuracy of VGG16, ResNet50, and Inception-V3 reached 95%, 92.5%, and 95.5% respectively. The performance of classification of these deep learning methods is much better than that of the shallow model, but manually detection and memory complexity during training still remain challenges. Several scholars have worked on the automatic detection and classification of breast cancer aiming to enhance the accuracy. For example, [PERSON] et al. [4] evaluated the use of the YOLO detector to perceive breast injury from mammograms. Three deep learning classifiers, i.e., Inception ResNet-V2, ResNet-50, and feedforward CNN are used to assess classification of breast lesions using a digital database for screening mammography (DDSM) and the INbreast database. With these classifiers, the method acquired an accuracy rate of 95.32%. [PERSON] et al. [8] used a CAD model (four-fold cross-validation) to estimate a full resolution convolutional network (FrCN) with X-ray mammograms using INbreast dataset. This approach achieved an accuracy of 95.96%, F1 score of 99.24%, and matthews correlation coefficient of 98.96%. [PERSON] et al. [19] proposed a diverse features (DFeBCD) method for breast cancer detection to classify mammograms as normal or abnormal. The IRMA mammography dataset uses two classifiers, a support vector machine (SVM) and an emotion learning-inspired integrated classifier (ELiEC). The ELiE classifier performance is superior to SVM and the accuracy rate reaches 80.30%. [PERSON] et al. [45] used the lifting wavelet transform (LWT) to obtain the region of interest features from the breast images. The size of the feature vectors diminish using a fusion of principal component analysis (PCA) and linear discriminant analysis (LDA) approaches. The extreme learning machine (ELM) and moth flame optimization approach is used for the classification using the DDSM and mammographic image analysis society (MIAS) databases. This approach attained an accuracy of 98.76% and 95.80% for DDSM and MIAS databases respectively. [PERSON] et al. [46] presented a breast cancer method based on diversity analysis, geostatistics and alpha form using the SVM classifier on DDSM and MIAS datasets and achieved the detection accuracy of 96.30%. [PERSON] et al. [47] presented an algorithm for segmentation utilizing intuitionistic fuzzy soft set and multigranulation rough set. The intuitionistic fuzzy soft set takes information from input images through various fuzzy membership operations. This approach deals with ambiguity between damaged and undamaged pixels when forming the membership function.