# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 2 Related Work
### Binary Classification of Breast Cancer
[PERSON] et al. [31] performed an early-stage analysis to detect breast cancer using the cmAssis algorithm, an AI-based deep learning CAD algorithm, to generate a successful result. As AI is used in cancer detection, its accuracy is based on several things. Researchers search for the effect of focus quality on the cancer detection algorithm's efficiency. The results showed a steady decrease in the efficiency of the cancer detector with the expected out of focus (OOF). [PERSON] et al. [32] developed a CNN (ConvFocus) to detect and rate OOF regions automatically at an accuracy level that suits a pathologist across the types of tissue, biopsy, and stain. [PERSON] et al. [33] proposed a system to predict breast cancer. The k-nearest neighbor (k-NN) classifier produced the highest accuracy compared with linear regression (LR) and support vector machines (SVM). In addition, researchers also used the filter method to pick the pertinent features from the available ones. This system reduces the cost of therapy because it can predict breast cancer at an early developmental stage. [PERSON] et al. [34] proposed a framework using _transfer learning_ to detect and classify breast cancer. The goal of transfer learning is to apply the information learned during the solution of one problem to another in a similar scenario. In the proposed framework, features have been extracted from breast cytology images using three different CNN architectures (e.g., ResNet50, GoogleNet, and VGG16). Using transfer learning, all CNN architectures are integrated to improve classification accuracy, and then the researchers used the average pooling classification to classify the cells into malignant and benign. [PERSON] et al. [35] suggested a shallow-deep CNN distinguish breast cancer cases, whether benign or malignant, by combining two models. Firstly, they developed a shallow CNN that forms virtual recombinant images from low-energy images. Secondly, they developed a deep CNN to extract new features from low-energy images. The ensemble models are then recombined to classify the case using a decision tree called a gradient-boosting tree. A new mammogram categorization model using CNN was implemented by [PERSON] et al. [36] for breast cancer detection, which categorizes cases into normal, malignant, and benign. They proposed two algorithms: convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). Their findings validate the importance and effect of the proposed model. [PERSON] et al. [37] suggested using super-resolution images to enhance the efficiency of CAD system texture analysis methods based on ultrasound images. To find tumors and differentiate between benign tumors. When a tumor is not spreading to the body, it is not cancer. It is classified as a malignant tumor when it can spread to other parts of the body [38]. Consequently, they demonstrated that their super-resolution-based approach increases the measured texture methods' efficiency and exceeds the state of the art to classify benign/malignant tumors [37]. A breast computer-aided diagnosis (CAD) approach was suggested by [PERSON] et al. [39] using mammogram images for the premature diagnosis, identification, and treatment of breast cancer based on feature fusion with deep CNN features. The mass detection process was based on deep sub-domain CNN features, and this approach was clustered by US-ELM to isolate the abnormal tissues. Using CNN, morphological features, texture features, and density features, the feature integration process incorporates deep features. After that, to distinguish benign and malignant breast masses, the mass diagnosis phase used ELM. The ELM has a better impact on classification than classifiers of multi-dimensional features. This experiment is superior to most existing methods of breast cancer diagnosis. [PERSON] et al. [40] proposed DenseNet, which is a deep learning classification model trained on 7173 historical tests before Sep 2017 and thereafter on 1194 instances.