The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review.

Simple Summary: Breast cancer is the most common cancer, which resulted in the death of 700,000 people around the world in 2020. Various imaging modalities have been utilized to detect and analyze breast cancer. However, the manual detection of cancer from large-size images produced by these imaging modalities is usually time-consuming and can be inaccurate. Early and accurate detection of breast cancer plays a critical role in improving the prognosis bringing the patient survival rate to 50%. Recently, some artificial-intelligence-based approaches such as deep learning algorithms have shown remarkable advancements in early breast cancer diagnosis. This review focuses first on the introduction of various breast cancer imaging modalities and their available public datasets, then on proposing the most recent studies considering deep-learning-based models for breast cancer analysis. This study systemically summarizes various imaging modalities, relevant public datasets, deep learning architectures used for different imaging modalities, model performances for different tasks such as classification and segmentation, and research directions. Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis. [ABSTRACT FROM AUTHOR]