ArticleBMC medical imaging2025
StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine learning in neuroradiology: Recent developments and applications.World journal of radiology · 2026Review
- Robust slice-level stroke classification in non-contrast head CT via structural consistency regularization and counterfactual suppression.Frontiers in bioengineering and biotechnology · 2026Article
- Integration of imaging and clinical biomarkers for cerebral infarction diagnosis via NeuroFusionNet.Frontiers in neuroscience · 2026Article
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Authors and funding
8 authors.
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Abstract
BACKGROUND AND
objectiveStroke ranks among the leading causes of disability and death worldwide. Timely detection can reduce its impact. Machine learning delivers powerful tools for image‑based diagnosis. This study introduces StrokeNeXt, a lightweight convolutional neural network (CNN) for computed tomography (CT) and magnetic resonance (MR) scans, and couples it with deep feature engineering (DFE) to improve accuracy and facilitate clinical deployment. MATERIALS AND
methodsWe assembled a multimodal dataset of CT and MR images, each labeled as stroke or control. StrokeNeXt employs a ConvNeXt‑inspired block and a squeeze‑and‑excitation (SE) unit across four stages: stem, StrokeNeXt block, downsampling, and output. In the DFE pipeline, StrokeNeXt extracts features from fixed‑size patches, iterative neighborhood component analysis (INCA) selects the top features, and a t algorithm-based k-nearest neighbors (tkNN) classifier has been utilized for classification.
resultsStrokeNeXt achieved 93.67% test accuracy on the assembled dataset. Integrating DFE raised accuracy to 97.06%. This combined approach outperformed StrokeNeXt alone and reduced classification time.
conclusionStrokeNeXt paired with DFE offers an effective solution for stroke detection on CT and MR images. Its high accuracy and fewer learnable parameters make it lightweight and it is suitable for integration into clinical workflows. This research lays a foundation for real‑time decision support in emergency and radiology settings.
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