# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 2 Related Work
### Binary Classification of Breast Cancer  
[PERSON] et al. [22] review the limitations of machine learning in medical images, especially for cancer detection, which have been surpassed by the optimization of deep learning techniques. [PERSON] et al. [23] showed a three-dimensionality increasing in the identification of breast cancer, particularly abnormal and less malignant tumors. Both screening and identification mammography help screen the population. Three-dimensional (3D) mammography identified an increased rate of breast tumors in the axillary nodes. In addition, it also identified an increased number of breast cancers in the axillary lymph nodes. [PERSON] et al. [24] proposed a scheme to determine the tumor diagnostic performance of a digital breast biopsy in patients with non-calcified breast cancer. In this study, 106 females with invasive non-calcifying breast tumors who had DBT and FFD mammography were analyzed. Two radiologists who were blinded to the clinicopathological information assessed all DBT and FEDM pictures to determine the tumor grade (1-3) and analyzed the clinicopathological and imaging characteristics associated with the tumor visibility. Adding DBT to FEDM did not increase the early identification of non-calcified breast tumors in women with high-density breasts. [PERSON] et al. [25] advocated the merging of adversarial networks to deal with the mammography category imbalance and data scarcity. By merging the mass patches with the healthy breast pictures, a generative model is utilized to mimic mass development on normal tissue. The produced synthetic pictures are then used as complementary aberrant data to improve the stability of deep learning-based mass detector training and, consequently, the robustness of the final model. Breast mammograms contain synthetic masses, as positive samples may be generated using existing normal images. Extensive studies on the widely available breast dataset indicated a considerable increase in detection performance, demonstrating the usefulness of the suggested technique. [PERSON] et al. [26] elucidated the mechanisms underlying mammography screening's inability to identify second cancer in females with a personal history of breast tumors. Individuals with later tumor recurrence and monitoring mammography within one year of recurrence are included in the data. When cells grow out of control in the breasts and form a tumor, breast cancer starts. Breast cancer signs include an increase in breast size, fluid discharge from the nipple, pain, and a lump in the breast [27]. Detecting and classifying breast cancer in the early stages of its growth can allow patients to have successful treatments to improve survival and healing [20]. In optical mammograms, the method of mass detection is image preprocessing and image segmentation, which divide an image into regions such that in each field one or more properties are homogeneous. Feature extraction is the method of image segmentation used in the classification of micro-calcifications. Classifiers play a significant role in implementing computer-aided mammography diagnosis, and the selection of features (FS) is an essential part of any machine learning task [28]. In real life, the principal available methodology to examine breast cancer is mammography (which is a type of X-ray). It is based on human observation, knowledge, and perception. [PERSON] et al. [29] proposed image processing with the aid of ANN computation for computerized sign detection and breast cancer exploration with an accuracy of almost 99%. [PERSON] et al. [30] carried out a study to detect breast cancer using mammogram segmentation by using a double thresholding procedure to apply borders to the original image in the final segmented image. This approach is used for all biomedical images. One of its significant benefits is the reduction in processing time and storage area. The outcome indicates whether a person has breast cancer and shows the sections where the tumor is developing, providing satisfactory results for detecting breast cancer.