Deep learning for breast cancer diagnosis: A bibliometric analysis and future research directions

Breast cancer remains the leading cause of cancer-related deaths among women globally. Early detection of breast cancer significantly reduces the death rate and increases the chances of targeted treatment and recovery. Deep learning is one of the techniques used in breast cancer diagnosis to reduce the assessment period and false positives. Over the years, research on breast cancer diagnosis using deep learning has grown significantly. However, due to the large volumes of research publications in this area, it is challenging for interested researchers or clinical practitioners to understand thoroughly and objectively. Hence, to gain more clear insights into deep learning-assisted breast cancer diagnosis studies, a comprehensive bibliometric analysis has been conducted. The analysis of 4797 articles published between 2019 and 2023 including the subject areas, relevant outlets, institutions, countries, and keywords. Based on the review findings, there is continuous growth in deep learning-based breast cancer diagnosis research in China, the United States, and India which are the countries with the most research and strongest research collaboration on this subject matter. Additionally, a future research agenda to propel the advancement of deep learning in breast cancer diagnosis is suggested. This review serves as a valuable guide for researchers interested in the potential of deep learning in breast cancer diagnosis.