# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 1 Introduction  
Due to the anatomy of the human body, women are more vulnerable to breast cancer than men. Breast cancer is one of the leading causes of death for women globally [1; 2; 3; 4] and is a significant public health problem. It occurs due to the uncontrolled growth of breast cells. These cells usually form tumors that can be seen from the breast area via different imaging modalities. To understand breast cancer, some basic knowledge about the normal structure of the breast is important. Women's breasts are constructed of lobules, ducts, nipples, and fatty tissues (Figure 1) [5]. Normally, epithelial tumors grow inside the lobes, as well as in the ducts, and later form a lump [6], generating breast cancer. Breast abnormalities that can indicate breast cancer are masses and calcifications [7]. Masses are benign or malignant lumps and can be described in terms of their shape (round, lobular, oval, and irregular) or their margin (obscured, indistinct, and spiculated) characteristics. The spiculated masses are the particular kind of masses that have a high probability of malignancy. A spiculated mass is a lump of tissue with spikes or points on the surface. It is suggestive but not a confirmation of malignancy. It is a common mammography finding in breast carcinoma [8]. On the other hand, microcalcifications are small granular deposits of calcium and may reveal themselves as clusters or patterns (like circles or lines) and appear as bright spots in a mammogram. Benign calcifications are usually larger and coarser with round and smooth contours. Malignant calcifications tend to be numerous, clustered, small, varying in size and shape, angular, and are irregularly shaped [7, 9]. Breast cancer screening aims to detect benign or malignant tumors before the symptoms appear, and hence reduce mortality through early intervention [2]. Currently, there are different screening methods, such as mammography [10], magnetic resonance imaging (MRI) [11], ultrasound (US) [12], and computed tomography (CT) [13]. These methods help to visualize hidden diagnostic features. Out of these modalities, ultrasound and mammograms are the most common screening methods for detecting tumors before they become palpable and invasive [2, 14, 15, 16]. Furthermore, they may be utilized effectively to reduce unnecessary biopsies [17]. These two are the modalities that are reviewed in this article. A drawback in mammography is that the results depend upon the lesion type, the age of the patient, and the breast density [18, 19, 20, 21, 22, 23, 24]. In particular, dense breasts that are \"radiographically\" hard to see exhibit a low contrast between the cancerous lesions and the background [25, 26]. Digital mammography (DM) has some limitations, such as low sensitivity, especially in dense breasts, and therefore other modalities, such as US, are used [12]. US is a non-invasive, non-radioactive, real-time imaging technique that provides high-resolution images [27]. However, all these techniques require manual interpretation by an expert radiologist. Normally, the radiologists try to do a manual interpretation of the medical image via a double mammogram reading to enhance the accuracy of the results [28]. However, this is time-consuming and is highly prone to mistakes [3, 29]. Because of  
Figure 1: This scheme represents the anatomy of a woman’s breast. Inside the lobes are the zones where the epithelial tumors or cyst grow. Designed by Biorender (2020). Retrieved from [[https://app.biorender.com/biorender-templates](https://app.biorender.com/biorender-templates)]([https://app.biorender.com/biorender-templates](https://app.biorender.com/biorender-templates)). these limitations, different artificial intelligence algorithms are gaining attention due to their excellent performance in image recognition tasks.