Abstract
Simple Summary Breast cancer was diagnosed in 2.3 million women, and around 685,000 deaths from breast cancer were recorded globally in 2020, making it the most common cancer. Early and accurate detection of breast cancer plays a critical role in improving the prognosis and bringing the patient survival rate to 50%. Deep learning-based computer-aided diagnosis (CAD) has achieved remarkable performance in early breast cancer diagnosis. This review focuses on literature considering deep learning architecture for breast cancer diagnosis. Therefore, this study anchors a well systematic and analytical review from six aspects: the model architecture of breast cancer diagnosis, datasets and image pre-processing, the manner of breast-cancer imaging, performance measurements, and research directions. Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.