Early 2024 Research on Recent Advances in the Diagnosis of Breast Cancer Leveraging Deep Learning Techniques

Abstract

Breast Cancer (BrC) is still a serious worldwide health issue, requiring innovative methods of early detection to enhance patient outcomes. If BrC is detected and treated early, there is a strong chance that the patient will recover. In order to predict the growing of cancer cells using medical imaging modalities, a number of researchers have developed automated DL-based approaches that are effective and accurate. There are currently very few review studies that provide an overview of some of the existing research on BrC diagnosis. Emerging architectures and modalities in the diagnosis of BrC, however, were not covered by this research. This review canters on the developing deep learning (DL) architectures for the detection of BrC. However, were unable to address new modalities and architectures in the diagnosis of BrC. The developing DL architectures for BrC detection are the main topic of this review. The survey that follows outlines current DL based architectures, evaluates the advantages and disadvantages of previous research, looks at the datasets that have been used, and goes over image pre-processing methods. This study supports ongoing efforts by the global healthcare community to improve BrC outcomes by utilizing state-of-the-art technology to enable timely and effective detection. It also presents research directions for future researchers, challenges, and performance metrics and results.