# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 2 Related Work
### Binary Classification of Breast Cancer
Following detection, the cells are sorted into two categories: normal and cancerous, using a 10-fold cross-validation procedure. [PERSON] et al. [57] discovered that MRI has the highest sensitivity for detecting benign and malignant tumors. Consequently, they developed and validated a two-step CAD technique using DCE-MRI images to identify infiltrated from situ breast tumors. The first phase employs a threshold algorithm to find tumor-like patches. Using a machine-learning technique for feature extraction, the second stage classified the site as invasive breast cancer. The ROI Hunter software recognized 75% of tumor volumes during the segmentation process, but it also had an interactive feature that allowed the manual insertion of regions overlooked by the automatic detection/segmentation procedure. [PERSON] et al. [58] utilized the images of breast cancer slides in this research. To train and assess the model, researchers used two freely available datasets from the Internet. The images were randomly sampled into patches, and this study contributes to the reduction of false positives by increasing the ratio of these patches in the sample. As a result, the AUC and efficiency both improved significantly. [PERSON] et al. [59] compared 101 radiologist evaluations of DM test groups against an AI system's ability to diagnose malignancy. Each DM exam was scored by a radiologist, and the real detection rate was calculated. In total, there were 7 malignancies and 52 false positives among the 5082 screenings. In the seven tumors discovered through screening with low-risk ratings, all but one were considered clearly visible. [PERSON] et al. [60] The application of artificial intelligence to identify benign breast mammography images in the context of breast cancer detection is discussed and evaluated in this article. One of the models proposed by [PERSON] et al. was investigated. The proposed artificial intelligence system is capable of accurately determining if a breast is cancer-free while also significantly lowering the number of false positives. As a result, artificial intelligence could boost digital mammography's reliability. [PERSON] et al. [61] report that multi-layered CNNs have made significant advancements in AI-based computer systems for detecting breast cancer risks. Mammography reveals the location of a suspicious breast mass. As a result, any method that involves automated DM image assessment should flag any suspect findings. The study suggested therapeutic applications for AI-assisted digital breast tomosynthesis interpretation and decision-making. Transpara is a mammography-based automated breast cancer detection system. The system was employed by [PERSON] et al. [7] The main functionality of the system is that it uses CNN and image analysis by combining data from various locations, knowing that each location has a value between 1 and 100 that represents the likelihood of malignancy (with 100 representing the highest suspicion). A final algorithm compares all locations discovered in the right and left breast images from 1 to 10, with 10 being the most likely to have cancer. [PERSON] et al. [62] AI-based and FDA-approved CAD algorithms were evaluated in terms of false positives per image (FPPI) on mammograms. A retrospective review of 250 full-field digital mammograms was conducted. Cases with no AI-CAD markings made up 48% of the total, while only 17% of the cases had conventional CAD markings. Research has shown that decreasing FPPI by 69 percent can reduce the reading time per case by 17%. [PERSON] et al. [63] review the approaches recently considered to identify breast cancer. According to the findings of this study, using mammogram images as a dataset is the most effective method of detecting breast cancer. Patients will receive less radiation, the operation will be less expensive, and most importantly, the technology will be more accessible. Further, when it comes to classification, other methods are regarded as less accurate than sequential minimal optimization. According to [PERSON] et al.