# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 2 Related Work
### Binary Classification of Breast Cancer
Following that, model selection selects the top three outcomes based on their validation accuracy throughout the validation phase. The results of the experiments show that combining the best models (ensemble networks) results in over 80% classification accuracy and area under the curve. [PERSON] et al. [50] introduced a unique computer-assisted diagnosis technology. The proposed CAD system can detect and classify simultaneously. The system was trained and evaluated using a public dataset. The method distinguishes benign from malignant tumors with high accuracy. For detecting breast cancer, [PERSON] et al. [51] proposed a novel high-accuracy technique. This approach includes two stages. The first stage is preparing input images (mammography) for feature and pattern extraction using image processing methods. In the last stage. The collected features are then fed into two kinds of supervised learning models: the back propagation neural network (BPNN) model and the logistic regression (LR) model. According to the results, the LR model used more characteristics than the BPNN. [PERSON] et al. [52] compared three widely used machine learning algorithms and approaches for breast cancer prediction: Random Forest, k-NN (k-Nearest-Neighbor), and Naive Bayes. As a training set, the Wisconsin Diagnosis Breast Cancer data set was used to assess the performance of several machine learning algorithms in terms of essential metrics such as accuracy and precision. The random forest, k-NN, and Naive Bayes had exceededthe accuracy of 94.7%, 95.9%, and 94.4%, respectively. To predict breast cancer, [PERSON] et al. [53] proposed a deep neural network with feature selection algorithms. The proposed method is evaluated using several assessment benchmarks, such as train accuracy. The suggested method's simulation results are promising, with an accuracy of 99.42%. The proposed technique is considered efficient and exact in predicting breast cancer based on experimental simulations and statistical data analysis. The emergence of computer-assisted diagnostics programs aided radiologist mammography screening significantly. Since RCNN is computationally expensive, faster R-CNN was combined with an integrated mammographic CAD in this work by [PERSON] et al. [54] for simultaneous mass detection and segmentation. Bordering the lesions boxes with information on the lesions positions and sizes was performed for breast cancer detection and categorization of malignant or benign lesions. The bordered boxes were manually labeled by an expert radiologist for the image preprocessing part, and the CAD used a vertical and horizontal rotation of images to enhance the training dataset. This retrospective study by [PERSON] et al. [55], which is based on artificial intelligence prediction models and _ResNet_ as its foundation, proves the integration of mammography characteristics extraction and cancerous and non-cancerous regions detection in the image. To enhance the data quality, contrast adjustment, and background cropping with text removal (BCTR) have been proven to increase the performance of pre-trained models. Furthermore, using more training data shows a consistent improvement in performance. The study used mammographic images that were annotated by radiologists to show the regions of proven cancer by biopsy. This study by [PERSON] et al. [8] developed an approach--a prediction algorithm capable of effectively establishing the diagnosis on screening mammography that can aid radiologists. This method trained the images in two ways: the first one used the mammograms' interpretations by the radiologists, while the second one only relied on the cancer status that was mentioned on the mammogram image. [PERSON] and [PERSON] [56] used a deep-CNN approach to contribute to the detection, segmentation, and labeling of cell nuclei in breast tumors using anisotropic diffusion and a multilevel model in detecting the nuclei, which were then combined to produce a highly accurate nMSDeep-CNN model with a noticeably short computation time.