# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 2 Related Work
### Binary Classification of Breast Cancer
[76] developed the DLA-EABA for breast tumor diagnosis utilizing sophisticated computational techniques and classic computer vision technologies. The system employs CNN-based transfer learning to perform diagnostic and prognostic tasks on breast tumors. Compared with other systems, the experimental system showed an excellent accuracy of 97.2%. [PERSON] et al. [77] developed ML methods for classifying cancerous and benign tumors, in which the system learns from prior information and can anticipate the class of the newest input. The performance of ML methods was evaluated using Wisconsin cancer datasets. The experimental findings show that SVM methods produce greater accuracy. [PERSON] et al. [78] suggested a DL and NN classifier for feature extraction, and the DDSM 1 dataset was used. The proposed ensemble model uses deep learning-based, pre-trained image processing. The NN was utilized to maximize the robust features retrieved from the ensemble models. The work was conditioned to distinguish benign from malignant tumors. It results in an accuracy of 0.88. [PERSON] et al. [79] emphasized a detection system based on CNNs that utilizes DL to categorize mammogram pictures into benign, cancerous, or normal. The suggested approach uses CNNs to classify breast masses using medical image processing. The findings are compared to the k-NN classifier. The experiments employed three datasets. The proposed CNN model has higher accuracy with the three datasets. [PERSON] et al. [80] evaluated the YOLO-V3 detector for breast cancer detection and classification. First, it converts the DICOM-formatted mammograms from the breast dataset to images without sacrificing any information, then it finds cancer in FFDM and automatically distinguishes between malignant and benign lesions. The feature extractors are ResNet and Inception. The outcomes are that the YOLO-V3 system is the most effective, and to achieve high accuracy, YOLO's classification network is replaced by ResNet and Inception-V3. [PERSON] et al. [81] provided a system-supported mammography image for the purpose of early cancer detection. The suggested system was assessed using a knowledge set of 500 mammography pictures and a CNN model for training and testing. Deep learning technology was utilized to detect changes in tissue in the mammography images evaluated. On the other hand, the suggested technique estimates risk factors for unaffected individuals and uses this risk factor as a monitoring indicator for at-risk patients. The gathered data illustrates the suggested system's efficiency in terms of high accuracy and a decrease in the effort required by medical professionals to help patients. [PERSON] et al. [82] used a classification system that consisted of three primary stages. The dataset is first extracted using image processing algorithms, and then it is subjected to data pretreatment procedures. Furthermore, they employ categorization algorithmsbased on machine learning. The dataset consisted of 34 individual patients, some of whom had numerous lesions while others had only one. The model has the highest level of precision, which is 0.94. Deep learning using a YOLO detector should be implemented and investigated for breast lesion identification from mammograms, according to [PERSON] et al. [83] There are 2620 patients in the dataset, which is divided into 43 volumes. Each instance had four mammograms, each with two distinct breast slides (i.e., MLO and CC). The maximum accuracy was 0.99, while the average was 0.97. The only important disadvantage is that CAD systems were used to detect breast cancer. [PERSON] et al. [84] investigated how machine learning techniques affect mammography image classification automation. Breast cancer is classified and identified using machine learning. They classified the dataset into three stages: pre-processing, feature extraction, and classification. They needed specified features collected and picked from mammography pictures (intensity, size, shape, and texture). The method's outcome has an accuracy of 0.94. [PERSON] et al.