Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches

To provide a critical and clinically oriented synthesis of recent deep learning developments for breast cancer imaging across major modalities, with emphasis on model architectures, dataset characteristics, methodological quality, and implications for clinical translation.

Following PRISMA guidelines, we systematically searched PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar for studies published from 2020 to 2024 on deep learning applied to breast imaging. Sixty-five studies using convolutional neural networks (CNNs), Transformers, or hybrid architectures were included. Datasets were comparatively profiled, and study quality and risk of bias were appraised using QUADAS-2.