# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 1 Introduction  
Following cardiovascular diseases, cancer has been the second-leading cause of human deaths on our planet. Based on the Saudi Ministry of Health, it is the most frequent type of cancer among Saudi women, which could also be observed in men but rarely [1]. It has been observed that the eastern region of Saudi Arabia has the highest breast cancer rate [2]. It has also been observed that the hereditary factor in breast cancer accounts for less than 10% of the total recorded cases. Within the regional context, the eastern region exhibits the highest incidence rate of 48%. It is worth noting that breast cancer is the leading cancer affecting women worldwide, with an annual incidence exceeding two million cases, which results in over half a million deaths each year. Over the past few decades, there have been significant advances in computing machines in terms of both hardware and software. Today, most desktop PCs are equipped with large on-chip caches, GPUs, and multicore CPUs to efficiently process computationally expensive data, e.g., images and/or videos. Due to these advances, researchers aroundthe globe are using these machines for machine learning applications such as artificial intelligence. With the advent of the latest deep learning technologies, it has now become practical to train different learning models (e.g., convolutional neural networks (CNN)) on large-scale datasets. No doubt, the field of deep learning has significantly evolved over the past few years. In this regard, the research community has developed numerous robust and resilient algorithms. However, researchers also developed different large-scale image and video datasets for the purpose of training deep learning models. Most importantly, some developers went one step ahead and trained many deep CNN models (e.g., Xception, VGG16, ReseNet50, GoogleNet, and SqueezeNet) on large-scale datasets to learn about features of a vast variety of images such as cups, planes, watches, flowers, mice, and others. These trained CNN models (called the _pretrained deep CNN (P-DCNN) model_) are now open for the public to use for any other application. With the approach of _transfer learning_, the P-DCNN models can be well-trained even on small image datasets, thereby bringing slight modifications to their last 2D-convolutional and classification layers. Due to this reason, today the medical imaging community and researchers are both trying their level best to use these deep learning schemes to increase cancer screening accuracy. Though breast cancer is serious and challenging to treat, it is preventable if detected in the preliminary stages, as compared with other cancers. According to the Saudi Ministry of Health, early detection using mammography may significantly reduce the mortality rate, e.g., up to 30%. However, its late detection may worsen the patient's condition and result in the patient's death [3]. Based on the spirit of these highlights, this study is strongly focused on the following aspects:  
* _Reviewing the state-of-the-art in the breast cancer paradigm._
* _Surveying the standard image processing breast cancer detection schemes._
* _Analyzing contemporary deep learning schemes for cancer detection._
* _Training DCNN models on the local hospital (King Fahad University's Hospital Medical) data and reporting the final classification accuracy as well._
* _Developing a fully automated tool to extract keywords describing the mammogram images according to the BI-RADS descriptors from the unstructured report and then using it to classify the resultant breast cancer, if any._
* _Building two models for detecting breast cancer: one using mammogram images and the other using clinical reports._  
Figure 1a-c shows three types of breast cancer: benign, malignant, and normal mammograms, respectively. The early detection of breast cancer using a mammogram is an essential tool.