Recent Advancements in Breast Cancer Detection: A Holistic Review of Microwaves, Ultrasound, and Photo-Acoustic Imaging Techniques

Abstract

Breast Cancer Detection (BCD) presents persistent challenges in clinical practice, with patients often experiencing quite high mortality risks due to diagnostic inaccuracies driven by imaging complexities and variable breast densities. High false positive and negative rates associated with traditional imaging modalities such as magnetic resonance imaging and mammography highlight critical limitations, including radiation exposure, patient discomfort, high cost, and non-uniform feature extraction. These shortcomings necessitate the development of innovative, non-invasive, and cost-effective alternatives. This paper reviews recent advancements in emerging BCD modalities, including microwave imaging, ultrasound, and photo-acoustic imaging. It examines their principles, technological progress, and potential for clinical adoption. Furthermore, it evaluates the integration of artificial intelligence in BCD, focusing on lesion segmentation, which remains underexplored compared to classification tasks. This study critically examines over 68 research papers published since 2017 and emphasizes the benefits and drawbacks of the proposed designs. The findings aim to advance the design and implementation of reliable, patient-centered technologies, addressing key gaps in diagnostic precision and contributing to the biomedical engineering domain.