# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 2 Related Work
### Multi Classification of Breast Cancer: Analysis and Discussion  
Numerous examinations have shown the effectiveness of the CNN model for multi-class breast cancer classification. For example, [PERSON] et al. [86] used the balanced public dataset to assess two machine learning breast cancer detection approaches. It uses the Humoment and Harlicka textures for the first approach. In addition, pre-existing networks are employed as feature extractors and model-based models for the second approach. The pre-trained network with SVM is the best for breast classification. [PERSON] et al. [87] combine Gabor, wavelet, and structure-based information set features to improve mammography cancer diagnosis. To construct a ubiquitous information set, the intuitionistic fuzzy set is introduced. These characteristics are assessed on annotated private and public datasets using the hesitancy-based Hanman transform classifier. This classifier embodies and models the uncertainty in errors between training and test features. The proposed techniques achieve 100% accuracy for multi-class classification on public and private datasets, which is superior to existing methods. It will help radiologists discover breast cancer earlier. In addition, breast problems such as calcifications, lumps, asymmetry, and carcinomas may be better managed with deep CNN. First, [PERSON] et al. [88] used ResNet50, i.e., P-DCNN. Then, an improved deep learning model was created, in which the learning rate is one of the essential qualities while training the neural network. The suggested approach adapts the learning rate depending on changes in error curves throughout the learning process. The model classified masses, calcifications, carcinomas, and asymmetry mammograms with an accuracy of 88%. [PERSON] et al. [89] use deep learning and ultrasound pictures to screen for breast cancer. Based on the experimental results, deep learning may aid radiologists in clinical applications by accurately predicting clinical outcomes from 2D B-mode ultrasound images. The suggested technique achieves 96.31% accuracy, 92.63% sensitivity, and 96.71% specificity. It can process ultrasound picture frames per second, making it a very efficient machine. Thistechnique outperforms earlier methods in real-time computer-aided diagnosis of breast tumors and benign-malignant categorization. [PERSON] et al. [90] used a two-stage deep learning and machine learning architecture to classify breast digital pathology pictures into normal tissue, benign lesions, ductal carcinoma in situ, and invasive cancer. The suggested technique reached an overall accuracy of 85.19% for multi-classification and 96.30% for binary classification (abnormal versus normal). The suggested two-stage method might be used to classify breast pathology pictures and help pathologists diagnose breast cancer. [PERSON] et al. [91] developed a network to increase its breadth. Histopathological breast image categorization using a unique two-branch deep CNN as invasive cancer, in situ carcinoma, benign lesion, and normal tissue imaging is classified by the network using a public dataset. An advantage of the proposed network is that gradient propagation is possible. The proposed network outperforms earlier approaches by 83.6% by dividing the training set into patches and images. The public's unseen test images were also classified with 89.4% accuracy. [PERSON] et al. [92] are scaling original images to build the CNN model and classify breast cancer. The model categorizes and finds breast cancer images from the public dataset. There are four benign and four malignant subclasses in this set. The proposed approach will automatically classify these breast cancer images into eight classifications, potentially saving lives globally. A new method for SURF selection by [PERSON] et al. [93] included preprocessing and intrinsic feature extraction using K-mean clustering. A decision tree model outperforms the recommended automated DL approach using K-mean clustering with MSVM.