Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review

Abstract

Breast cancer (BC) is the leading cause of death among women worldwide. Early detection and diagnosis of BC can help significantly reduce the mortality rate. Ultrasound (US) can be an ideal screening tool for BC detection. However, the hand-held US (HHUS) is an impractical tool because it is operator-dependent, time-consuming, and increases the likelihood of false-positive results. Thus, to address these issues, the 3D Automated Breast Ultrasound System (ABUS) was designed for BC detection and diagnosis. This paper presents the transition from traditional approaches to deep learning (DL) based CAD systems in the ABUS image data set. The capabilities and limitations of both techniques are also reviewed rigorously. This review will help in understanding the current limitations to leverage their potential in diagnostic radiology to improve performance and BC patient care.