# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 2 Related Work
### Binary Classification of Breast Cancer
For the cross-validation and test sets, receiver operating characteristics generated by the DenseNet model predictions were greater than those generated by radiologist assessments. The DenseNet model produces excellent diagnostic performance, with an AUC score of 79%. [PERSON] et al. [41] demonstrated breast cancer detection by classifying mammograms as malignant or benign using a range of variables. The proposed algorithm collects features from mammography pictures using a DCNN based on a highway network. For the purpose of identifying breast masses via the use of textural description, spectral clustering, and support vector machines, [PERSON] et al. [42] proposed a full process-integrated technique for constructing a CAD system. Breast regions of interest are discovered automatically from mammogram images using gray-scale improvement and data cleaning to achieve this. [PERSON] et al. [43] showed the importance of machine learning. The recent improvement in AI-based image analysis may help in routine pathology de tection. These studies may help pathologists make more exact and prompt diagnoses of patients. Furthermore, GradCAM finds previously unknown essential histological features for newer pathological findings and research goals for disease comprehension. To enhance the accuracy of breast cancer detection, [PERSON] et al. [44] examined feature fusion diagnosis using images from several modalities. Two hundred and one individuals with mammography and breast MRIs were retrospective. When compared to a single modality, the accuracy of MRI is superior to that of mammography. [PERSON] et al. [45] claimed that an extreme learning machine (ELM) is a form of ANN with great promise for classification. Three academic disciplines are brought together in the scope of this research paper: For starters, ELM is used in the detection of breast cancer in women who have already been diagnosed. After that, characteristics of no consequence are omitted using the gain ratio feature selection strategy. Finally, a remote breast cancer diagnostic technique is proposed that uses ELM, a cloud computing-based technology. The cloud-based ELM's performance compared to that of many innovative illness detection systems. The WBCD data set proves that the cloud-based ELM technique beats the other models. Both the standalone and cloud settings, which were evaluated, yielded excellent performance results for ELM. The most important outcomes of the experiment show that the accuracy attained was high. [PERSON] et al. [46] proposed a new novel. The suggested method uses adaptive fuzzy-based median filtering to remove noise from mammogram images. Image segmentation is utilized by multi-level thresholding. An extractor model of cabinet-based features is proposed for the detection of breast cancer with this new detection approach. [PERSON] and [PERSON] [47] utilized a suitable threshold based on the Legendre neural network with a single layer and developed a computational technique for detecting and segmenting breast cancer. The three main steps of the method are image preprocessing, segmentation of mammograms using adaptive thresholding, and post-processing. The suggested segmentation method has a training stage with thirty images and a testing stage with 151 images from the MIAS standard database. The proposed model has high accuracy. [PERSON] et al. [48] discussed two methods. In the first technique, pre-trained models were developed from scratch. The second approach employs the same principles as the first, except that all models have been fine-tuned via transfer learning. For this effort, the research explored convolutional neural network modifications. Experiments were conducted on two distinct datasets, and both datasets showed superior performance from the fine-tuned network. [PERSON] et al. [49] utilized deep ensemble learning. The proposed approach is built upon _AlexNet_ with modifications to fit the classification problem.