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

Overview

1. Department of Systems Engineering, Pontificia Universidad Javeriana, Bogotá 110231, Colombia

Abstract

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

• kaggle.com/datasets/crawford, at Kaggle; found in “Data Availability Statement”
• physionet.org/content/bhx-brain-bounding-box, at PhysioNet; found in the references
• physionet.org/content/ct-ich, at PhysioNet; found in “Data Availability Statement”

Data Availability Statement

The data used in this study are publicly available. The RSNA Intracranial Hemorrhage Detection dataset is accessible through the RSNA official challenge page at https://www.rsna.org/artificial-intelligence/ai-image-challenge/rsna-intracranial-hemorrhage-detection-challenge-2019 (accessed on 15 October 2025). The PhysioNet CT Images for Intracranial Hemorrhage Detection and Segmentation dataset is available at https://physionet.org/content/ct-ich/1.3.1/ (accessed on 21 November 2025). The qure.ai CQ500 dataset is available at https://www.kaggle.com/datasets/crawford/qureai-headct (accessed on 13 June 2026). No new data were created in this study.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 7 keywords, 54 references.

Cite

This paper

Peña Gutiérrez, J. R., & Bustacara Medina, C. J. (2026). A Resource-Efficient Hybrid Deep Learning Framework with External Validation for Intracranial Hemorrhage Detection in CT Scans. Diagnostics (Basel, Switzerland), 16(17), 2776. https://doi.org/10.3390/diagnostics16172776

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3390/diagnostics16111705

Cross-Dataset Generalization of Deep Learning-Based Detectors for Intracranial Hemorrhage Subtype Localization on Noncontrast Head CT: A Comparative Study.

Journal: Diagnostics (Basel, Switzerland)

In common: physionet.org/content/bhx-brain-bounding-box, other, 2 references

[2] doi:10.3389/fonc.2026.1834400

BrainFusionNet: an attention-augmented deep convolutional framework with hybrid loss optimisation and test-time augmentation for multi-class brain tumour detection in magnetic resonance images.

Journal: Frontiers in oncology

In common: 4 references

[3] doi:10.1016/j.patter.2026.101538 [code]

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Journal: Patterns (New York, N.Y.)

In common: 3 references

[4] doi:10.1371/journal.pone.0350637 [code]

Deep learning-based arterial waveform analysis for predicting postoperative cerebrovascular events in pediatric patients with Moyamoya disease.

Journal: PloS one

In common: 3 references

[5] doi:10.3389/fmed.2026.1804084

Lightweight deep learning framework for intracranial hemorrhage detection in brain CT scans.

Journal: Frontiers in medicine

In common: other, 2 references

[6] doi:10.1038/s41591-026-04497-1 [code]

Health system learning enables generalist neuroimaging models.

Journal: Nature medicine

In common: 2 references

[7] doi:10.3389/fmed.2026.1810860

Lightweight deep learning for medical imaging using MobileNetV2-based brain pathology classification with Grad-CAM interpretability.

Journal: Frontiers in medicine

In common: other, 2 references

[8] doi:10.3389/frai.2026.1849571

NeuroPlast: a learnable activation function evaluated under knowledge distillation for medical image classification.

Journal: Frontiers in artificial intelligence

In common: 2 references

[9] doi:10.1371/journal.pone.0344600 [code]

Robust disease prognosis via diagnostic knowledge preservation: A sequential learning approach.

Journal: PloS one

In common: 2 references

[10] doi:10.7554/elife.104053 [code]

Brain cognition gaps reveal associations with dopamine and factors related to brain health through artificial intelligence prediction of functional connectome.

Journal: eLife

In common: 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.