A Systematic Review on Screening, Examining and Classification of Breast Cancer

Breast cancer is one of the major causes of women's death. This leads to an immediate requirement for an earlier and accurate diagnosis to lower the mortality rate. Computer-aided detection (CAD) enables clinicians to effectively identify tumors. Herein, images are information sources used for the identification and diagnosis of breast tumors. Many imaging techniques allow clinicians for studying breast anatomy and its analysis. This paper intends to present a study that discusses the emerged algorithms and presents an overview of advancement in the detection and classification of breast tumors. The review also focuses on techniques that emerged for facilitating the classification of different masses using several imaging techniques. The study starts with an overview of several distinct machine learning, deep learning, and other models used for breast cancer diagnosis. Moreover, the study provides an outline of different imaging modalities and different publicly available benchmark databases employed in several research publications for the analysis of breast tumors. As a final point, the study summarizes the current trends and challenges lies in detecting and classifying breast tumors for future researchers.