Brain tumors classification using electrical bioimpedance spectroscopy based on a multi-scale feature extraction network with frequency band attention mechanism
< 0.05) between tumor types. In summary, the proposed framework, which integrates MS features and adaptive frequency, improves the performance of EBI-based tumor classification, and shows promise as an accurate intraoperative tool for the rapid diagnosis of brain tumors.
Authors
- Zhuoqi Cheng (ORCID: https://orcid.org/0000-0002-4267-6928)
- Jing Guo
- Jiaxin Lu (ORCID: https://orcid.org/0009-0005-8395-1489)
- Qinglin Zheng
- Xiaobing Jiang
- Depei Li
- Yuqin Zhong (ORCID: https://orcid.org/0009-0008-4487-8882)
Institutions
- Guangdong University of Technology (CN)
- Sun Yat-sen University (CN)
- Maersk (Denmark) (DK)
- Guangdong Institute of Intelligent Manufacturing (CN)
- Sun Yat-sen University Cancer Center (CN)
Publication Details
- Journal
- Biomedical Physics & Engineering Express
- Published
- 2026-04-15
- DOI
- https://doi.org/10.1088/2057-1976/ae5f9e
- Primary Topic
- Electrical and Bioimpedance Tomography
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Scientific and Technological Planning Project of Guangzhou City
- Natural Science Foundation of Guangdong Province