Deep Learning Techniques for Breast Cancer Analysis: A Review

Abstract

Breast Cancer is one of the most frequent cancer among females worldwide. Despite considering medical advance-ments, Breast Cancer remains the world's second-largest cause of death; hence, early detection of this disease significantly impacts mortality reduction. Breast abnormalities, on the other hand, are complicated to diagnose. Deep learning is the most widely employed technique for accurate diagnosis. Breast Cancer screening technologies such as mammography, ultrasound, and MRI are used extensively. Using image processing and deep learning techniques, the computer-assisted diagnosis help radiologists in identifying problems more quickly. Deep learning algorithms exhibit the best outcomes since they extract the features of images deeply. Furthermore, radiomics analysis has the advantage of being used as a non-invasive method of characterizing tumours directly from clinical medical pictures. For cancer researchers, forecasting the survival rate of Breast Cancer patients is a severe challenge. The efficiency of Deep learning techniques has obtained much attention to provide reliable findings. We have done a brief review on current trends in Breast Cancer analytics using Deep learning techniques. The results are presented in tables to show how different strategies and their outcomes have changed over time.