A lightweight texture-based framework for stroke classification: a radiomics approach in resource-limited clinical settings

Abstract

Stroke is a leading cause of mortality and long-term disability worldwide. Rapid and accurate diagnosis is critical for effective management. Although manual diagnosis remains tedious and prone to potential errors, most existing automated analysis tools rely on complex architectures that are challenging to deploy in resource-limited clinical settings. To address this gap, we propose a novel lightweight decision-support approach for the preliminary classification of non-contrast brain CT scans into three categories: ischemic stroke, hemorrhagic stroke, and normal cases. Using an anonymized dataset from the Bafoussam Regional Hospital Center, 16 textural features (first-order and gray-level co-occurrence matrices) were extracted from manually targeted circular regions of interest to balance discriminative capability with computational efficiency. Evaluated using a lightweight Random Forest classifier under patient-level stratified Group K-fold cross-validation, the proposed model achieved a validated mean accuracy of 0.9928 (± 0.0101). Precision reached 1.00, while recall and F1-scores ranged between 0.99 and 1.00 across all classes within the validation setup. The consistency of these results across validation folds suggests that the extracted texture features provide distinct signatures for the targeted stroke subtypes within this cohort. These performances demonstrate the feasibility of this radiomics-based approach. As a proof of concept, our approach shows promise as a preliminary decision-support tool with potential for future clinical workflows. It is promising for resource-limited settings where access to expensive and high-computational neuroimaging software is restricted. Not applicable.