Recent trends on mammogram breast density analysis using deep learning models: neoteric review

Abstract Breast cancer is a globally prevalent and potentially fatal illness affecting women. Timely identification of screening mammography may decrease the occurrence of incorrect positive results and enhance the rate of patient survival. Nevertheless, the density of breast tissue in mammograms can impact the precision and effectiveness of detecting breast cancer. This paper examines the existing body of research on the analysis of breast density in mammograms utilising advanced deep learning models, including convolutional neural networks (CNN), transfer learning (TL), and ensemble learning (EL). Additionally, it examines various datasets and evaluation measures employed in the investigations. The study demonstrates that deep learning models can attain exceptional accuracy in categorising breast density. However, they encounter obstacles such as limited data availability, intricate model structures, and difficulties in interpreting the results. The research asserts that categorising breast density is an essential undertaking in order to enhance the identification and survival rates of breast cancer. Further investigation is warranted to examine the most effective deep learning structures, data augmentation methods, and interpretable models for this undertaking.