Breast mass segmentation using mammographic data: a systematic review

Abstract

ABSTRACT Breast cancer continues to be the most prevalent cancer infecting humans worldwide. Timely detection can assist in the reduction of both mortality rates and treatment costs. Segmentation plays a crucial role in the timely detection of breast cancer. This study describes the methodology and findings of a systematic review (SR) that aimed to evaluate cutting-edge segmentation approaches in computer-aided diagnosis (CAD). The primary objective is to provide a general overview and analysis of each of these methods, as well as to address the limitations and difficulties associated with them. Based on the observed shortcomings, directions for future investigation have also been offered. In addition, this SR also provides an overview of numerous publicly available benchmark datasets used in the reviewed literature. Unsupervised algorithms have been found to be more commonly used than supervised and DL counterparts. DL techniques require powerful computers and enormous volumes of training data. In most of the existing segmentation approaches, digitised images from DDSM and mini-MIAS datasets have been employed for experimentation. Both medical professionals and researchers who are aiming to address research gaps and make significant contributions to breast cancer diagnoses may find this systematic review to be valuable.