Attention-enhanced deep learning model for automated classification of hemorrhagic and ischemic stroke on CT imaging
GCDCNet demonstrates superior accuracy, lightweight, and good generalization ability in the intelligent classification task of stroke CT images through lightweight design, feature optimization, and enhanced interpretability. This model can be used to assist clinicians in early stroke screening, emergency triage, and telemedicine applications, improve stroke diagnosis efficiency, and optimize medical resource allocation.
Authors
- Peipei Du
- Yue Hu
- Siyao Che
- Zihan Wang
- Yuanyuan Chen
- Siyu Che
- Xiaofei Liu
- Lingbo Kong
Institutions
- Shanxi Medical University (CN)
- Beijing University of Chinese Medicine (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Beijing University of Technology (CN)
- Chinese Institute for Brain Research (CN)
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine (CN)
- Guang’anmen Hospital (CN)
- Shanxi Academy of Medical Sciences (CN)
- Tongji Hospital (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2026-03-03
- DOI
- https://doi.org/10.1186/s40001-025-03784-w
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00