From Data to Diagnosis: Narrative Review of Open-Access Mammography Databases for Breast Cancer Detection

Abstract

Breast cancer remains a significant global health challenge, necessitating advancements in screening and diagnostic methods for its early detection and treatment. This review explores the role of open-access mammography databases in facilitating research and development in the field of breast cancer detection, particularly through the integration of artificial intelligence techniques such as machine learning and deep learning. In this review, we highlight the open-access databases, including the Digital Database for Screening Mammography (DDSM), the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), Mini-DDSM, INbreast, Mammographic Image Analysis Society Dataset (MIAS), and the China Mammography and Mastopathy Dataset (CMMD). Each database was analyzed in terms of its composition, features, limitations, and contributions to breast cancer research. In addition, we highlight the importance of open-access databases in enabling collaborative research, improving algorithm development, and enhancing the accuracy and efficiency of breast cancer detection methods computer-aided diagnosis.