Deep Learning Meets Bibliometrics: A Survey of Transfer Learning Techniques for Breast Cancer Detection

This study aims to provide a comprehensive biblio-metric analysis of research on transfer learning in breast cancer detection from 2016 to 2024. It highlights publication trends, influential contributors, collaborations, and keyword patterns. Bibliometric methods are employed to analyze data extracted from the Scopus database. It includes co-occurrence and citation analyses to identify prevalent keywords, highly cited documents, journals, authors, organizations, and countries contributing to this field. The analysis reveals a significant upward trend in publications over the last decade. Key insights include the identification of dominant keywords, influential contributors, and notable collaborations. The results highlight the growing impact of transfer learning techniques in breast cancer detection research, particularly within the domains of medical imaging analysis and predictive analysis. This study offers a systematic overview of the current state of transfer learning in breast cancer detection research, providing valuable insights and guiding future research efforts in this rapidly evolving domain.

@article{Wajid2025,
  title     = {Deep Learning Meets Bibliometrics: A Survey of Transfer Learning Techniques for Breast Cancer Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Amna Wajid and Natasha Nigar and Hafiz Muhammad Faisal and Olukayode Oki and Jose Lukose},
  doi       = {10.14569/IJACSA.2025.0160874},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160874}
}