Breast diseases pose a significant threat to women's health, so early detection and treatment are extremely important. In this context, early disease identification has become crucial in the diagnosis and treatment of breast
diseases. This paper begins by outlining the pivotal role of mammography in the early diagnosis of breast cancer,
comparing the structural similarities and differences between normal and diseased breast tissues. This comparison underscores the primary role of mammography in the diagnosis and treatment of breast diseases. Additionally, our paper provides an overview of fundamental concepts related to breast cancer detection, diagnosis, and
prediction systems. It delves into the latest research developments in auxiliary diagnostic detection, examination,
and risk prediction systems associated with breast cancer. Our objective is to offer a comprehensive understanding of the role of computer-aided detection, diagnosis, and prediction systems in breast diseases, fostering further
development and application. This work aims to explore and drive innovation in the field, enhance early detection
rates of breast diseases, and guide readers towards novel directions, thus contributing to female healthcare management.
Keywords: Mammography, imaging, computer-aided diagnosis, deep learning, multi-modality