A Resource-Efficient Hybrid Deep Learning Framework with External Validation for Intracranial Hemorrhage Detection in CT Scans.

Background

Intracranial hemorrhage (ICH) is a time-critical neurological emergency in which delayed diagnosis significantly worsens patient outcomes. This challenge is amplified in resource-limited, high-workload settings where rapid neuroimaging interpretation may be constrained. Many high-performing deep learning approaches for disease detection rely on large-scale models trained on extensive data and evaluated only on internal datasets, limiting their generalization across heterogeneous clinical environments. This study aims to develop and evaluate a resource-efficient framework for automated ICH detection from CT scans, with internal and external validation across heterogeneous clinical settings.

Methods

A hybrid deep learning framework was developed, combining an EfficientNetV2-S-based feature extractor with a bidirectional GRU model for scan-level prediction. The model was trained on a stratified subset of 6000 CT scans from the RSNA Intracranial Hemorrhage Detection dataset and evaluated using an internal test set and two external validation cohorts (PhysioNet and CQ500).

Results

On an internal held-out test set, the model achieved scan-level AUROC and AUPRC of 0.980 and 0.977, respectively, and slice-level AUROC and AUPRC of 0.981 and 0.923. External validation on the PhysioNet and CQ500 datasets yielded scan-level AUROC/AUPRC values of 0.914/0.932 and 0.905/0.909, respectively, demonstrating consistent performance across datasets differing in institution, geography, patient population, and acquisition protocols.

Conclusions

Despite its compact architecture and reduced training subset, the proposed framework achieves performance competitive with substantially larger and more computationally demanding models, completing training in under 26 h on single-GPU hardware. These results support the feasibility of reproducible, resource-efficient ICH detection systems for automated triage in emergency radiology workflows across different clinical settings.