## 2 Related Work
This reduces the distant pixels that are not in the region of interest, the diseased tissue in the mammogram is separated by a rough approximation of the fuzzy concept of the multiregional granulation space. [PERSON] et al. [48] developed an efficient adaboost deep learning method for the recognition of breast cancer. This work focused on the combination of various deep learning approaches with feature extraction and selection usingclassification and segmentation methods to evaluate the result and focus on finding the most suitable method and obtained an accuracy of 97.2%. [PERSON] et al. [49] utilized the gray level co-occurrence matrix (GLCM) and SVM classifier to detect breast cancer abnormalities on the MIAS database and achieved an accuracy of 93.88% and outperformed k-Nearest Neighbour (kNN). [PERSON] et al. [50] presented a use of the deep neural network for multilevel transfer learning for digital breast tomosynthesis. ImageNet knowledge first records mammogram information and then optimizes it during multi-step transmission of digital breast tomosynthesis information. Then the freezing of most of the convolution neural network (CNN) framework was compared to two transmission systems with the first convolution layer. [PERSON] et al. [51] proposed a deep active learning approach for the classification of breast cancer with the aim of maximizing the precision of learning with very limited labels. This approach manually annotates the most valuable unlabeled samples and integrates them into the training set. Then update the system repeatedly as the training set grows. Two deep active learning framework selection strategies are proposed i.e., confidence boosting strategy and an entropy based strategy on the breast cancer histopathological (BreakHis) database. This approach reduced the cost of annotation by up to 66.67%, which is more accurate than the standard query strategy. [PERSON] et al. [52] developed a patch-based CNN algorithm for the identification of lesions in breast in the full-field digital mammograms (FFDM) using a pre-trained CNN models (VGG16, ResNet50, and InceptionV3) for the extraction of features and attained the results of detection with a true positive rate (TPR) of 98%. [PERSON] et al. [53] developed an algorithm based on dilated semantic segmentation network (DICNN) with morphological operation. A DenseNet201 is used for the feature extraction. The feature vectors acquired from DenseNet201 and 24 layer CNN employing parallel fusion were fused to categorize the nodules. These feature vectors are merged with the SVM classification and attained an accuracy of 98.9%. [PERSON] et al. [54] developed a breast cancer detection method using breast thermal images. The two image features GLCM and local binary pattern (LBP) with heterogeneous weights are used to classify the breast thermal images into healthy and DCIS class by using the SVM and decision tree (DT) classifier. This algorithm had an accuracy of 92%. [PERSON] et al. [55] presented a joint thresholding (slime mould method and Shannon's entropy) and watershed segmentation method to improve and obtain the breast tumor from 2D MRI slices. The extracted breast tumor and ground truth image is executed and the essential image performance values (IPV) are calculated. Some of the existing works with dataset information and results are illustrated in Table 1.