AGTS-Net: Anatomically Guided Two-Stage Network for Actionable Stroke Classification in Complete NCCT Studies

Emergency stroke assessment using non-contrast computed tomography (NCCT) remains challenging because early ischemic signs may be subtle and anatomical variability across cranial scans can affect model performance. This study presents AGTS-Net (Anatomically Guided Two-Stage Network), an explainable deep learning framework designed to support stroke classification from complete NCCT studies. The framework was developed using a proprietary cohort of 99 patients, comprising 3440 NCCT slices annotated as hemorrhagic stroke, posterior ischemic stroke, anterior ischemic stroke, or no stroke. For each region-specific diagnostic classifier, data were split using the same scheme: 80% for training and 20% for testing, followed by a 20% validation split from the training subset. AGTS-Net first assigns each slice to a predefined anatomical region and then applies a region-specific classifier. Internal evaluation showed 0.99 anatomical-routing accuracy, and transfer learning experiments confirmed that anatomical regionalization improved classification over global models. A public Kaggle dataset of 7012 NCCT images was used only for external cross-domain evaluation of the anatomical pre-classifier, as compatible territorial diagnostic labels were unavailable. Grad-CAM maps visualized regions contributing to predictions. AGTS-Net is intended as a decision-support tool, providing actionable slice-level predictions and visual guidance during urgent stroke assessment.