RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer
The RSDCNet model presented in this study excels in the efficient and accurate classification of benign and malignant breast cancer pathology. Compared to traditional methods and other leading models, RSDCNet not only reduces computational resource consumption but also offers improved feature extraction and clinical interpretability. This advancement provides substantial technical support for the intelligent diagnosis of breast cancer, paving the way for more effective treatment planning and prognosis assessment.
Authors
- Gongsheng Jin (ORCID: https://orcid.org/0009-0000-6764-0940)
- Hongtao Li (ORCID: https://orcid.org/0000-0002-1290-0917)
- Haipeng Li (ORCID: https://orcid.org/0000-0003-2842-9012)
- Zhu Zhu (ORCID: https://orcid.org/0000-0003-0885-3123)
- Chen Chen (ORCID: https://orcid.org/0000-0002-3525-9755)
- Yuan Liu (ORCID: https://orcid.org/0009-0007-5944-3913)
- Xueji Zhang (ORCID: https://orcid.org/0009-0000-9811-4900)
Institutions
- Bengbu Medical College (CN)
- First Affiliated Hospital of Bengbu Medical College (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1177/20552076251336286
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48