A Scoping and Bibliometric Review of Deep Learning Techniques in Breast Cancer Imaging: Mapping the Landscape and Future Directions

A Scoping and Bibliometric Review of Deep Learning Techniques in Breast Cancer Imaging: Mapping the Landscape and Future Directions Publisher

Rezayi S¹ ; Nilashi M² ; Esmaeeli E³ ; Ramezanghorbani N⁴ ; Arji G⁵ ; Ahmadi H⁶ ; Shahmoradi L³ ; Zahmatkeshan M⁷

Show Affiliations

1. Department of Health Information Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
2. UCSI Graduate Business School, UCSI University, Cheras, Kuala Lumpur, 56000, Malaysia
3. Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
4. Iran Ministry of Health and Medical Education, Tehran, Iran
5. School of Nursing and Midwifery, Saveh University of Medical Sciences, Saveh, Iran
6. Faculty of Health, Centre for Health Technology, University of Plymouth, Plymouth, PL4 8AA, United Kingdom
7. Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran

Source: Neural Computing and Applications Published:2025

Abstract

In recent years, there has been a rise in interest in the application of deep learning (DL) to breast cancer identification. This work intends to deliver a review demonstrating new DL applications for identifying and categorizing breast cancer and offers a bibliometric analysis of advancements in this field. A comprehensive search between January 1, 2016, and February 20, 2023, was done on the Scopus database to perform the bibliometric analysis of DL applications in breast cancer detection. The statistics on publications, journals, authors, nations, and keywords were created using VOSviewer software. Moreover, Medline (through PubMed), Web of Science (WoS), and Scopus were used for the scoping review (from January 1, 2016, to December 24, 2022); all retrieved titles were checked for eligibility requirements by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. Since 2016, the frequency of papers being recovered has grown, and in 2022, a significant number of citations were retrieved. This paper gives a general overview of the various DL methodologies and tailored architectures for the detection, categorization, and segmentation of breast cancer. Performance of the approaches is assessed using accuracy, sensitivity, specificity, area under curve (AUC), F1 score, Dice similarity coefficient (DSC), and intersection over union (IoU); results with high accuracy are frequently obtained at the expense of sensitivity. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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Style	Citing Format
MLA	Rezayi S, et al.. "A Scoping and Bibliometric Review of Deep Learning Techniques in Breast Cancer Imaging: Mapping the Landscape and Future Directions." Neural Computing and Applications, vol. , no. , 2025, pp. -.
APA	Rezayi S, Nilashi M, Esmaeeli E, Ramezanghorbani N, Arji G, Ahmadi H, Shahmoradi L, Zahmatkeshan M (2025). A Scoping and Bibliometric Review of Deep Learning Techniques in Breast Cancer Imaging: Mapping the Landscape and Future Directions. Neural Computing and Applications, (), -.
Chicago	Rezayi S, Nilashi M, Esmaeeli E, Ramezanghorbani N, Arji G, Ahmadi H, Shahmoradi L, Zahmatkeshan M. "A Scoping and Bibliometric Review of Deep Learning Techniques in Breast Cancer Imaging: Mapping the Landscape and Future Directions." Neural Computing and Applications , no. (2025): -.
Harvard	Rezayi S et al. (2025) 'A Scoping and Bibliometric Review of Deep Learning Techniques in Breast Cancer Imaging: Mapping the Landscape and Future Directions', Neural Computing and Applications, (), pp. -.
Vancouver	Rezayi S, Nilashi M, Esmaeeli E, Ramezanghorbani N, Arji G, Ahmadi H, et al.. A Scoping and Bibliometric Review of Deep Learning Techniques in Breast Cancer Imaging: Mapping the Landscape and Future Directions. Neural Computing and Applications. 2025;():-.
BibTex	@article{ author = {Rezayi S and Nilashi M and Esmaeeli E and Ramezanghorbani N and Arji G and Ahmadi H and Shahmoradi L and Zahmatkeshan M}, title = {A Scoping and Bibliometric Review of Deep Learning Techniques in Breast Cancer Imaging: Mapping the Landscape and Future Directions}, journal = {Neural Computing and Applications}, volume = {}, number = {}, pages = {-}, year = {2025} }
RIS	TY - JOUR AU - Rezayi S AU - Nilashi M AU - Esmaeeli E AU - Ramezanghorbani N AU - Arji G AU - Ahmadi H AU - Shahmoradi L AU - Zahmatkeshan M TI - A Scoping and Bibliometric Review of Deep Learning Techniques in Breast Cancer Imaging: Mapping the Landscape and Future Directions JO - Neural Computing and Applications VL - IS - SP - EP - PY - 2025 ER -

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