aceAccurate classification of intracranial hemorrhage using transfer learning and combined spatial and channel attention modules
PurposeIntracranial hemorrhage (ICH), as a severe brain injury, requires immediate treatment and is usually being diagnosed from Computed Tomography (CT) scans in clinical settings. This task is considered a challenging procedure due to interpretation variability between experts, imaging modality limitations, time sensitivity, and heterogeneous presentations. Recent studies have utilized deep neural networks to overcome limitations such as overfitting and poor generalization across scanners, for ICH diagnosis wh…
A deep learning model combining attention modules with pre-trained networks outperformed baseline models at detecting and classifying six types of intracranial hemorrhage on the RSNA CT dataset.
The gap between the 97.8% average accuracy and the 73.3% average F1 score shows weaker performance on some measures, and the abstract reports results on one dataset only.
Evidence
- Peer-reviewedHealth Informatics Journal2026-10-26
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Truvace Impact Record TRV-2026-2283, v1: “Accurate classification of intracranial hemorrhage using transfer learning and combined spatial and channel attention modules.” Truvace, 2026-10-27. /record/TRV-2026-2283 (accessed at citation time). sha256 fe53ce3f91167032…
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