A lightweight hybrid feature extraction method for stroke segmentation, classification, and prediction in brain CT scans using multilayer perceptron

Abstract Purpose Timely and precise classification of stroke types is critical for immediate clinical intervention. This study aims to propose a lightweight dual-domain pipeline optimized for three-class ROI-level computed tomography (CT) stroke classification, bridging the gap between automated analysis and clinician-led diagnosis in resource-constrained settings. Methods We introduce a computationally efficient hybrid feature extraction method combining discrete wavelet transform (DWT) and gray-level co-occurrence matrix (GLCM), leveraging a synergistic multi-scale textural analysis. Images are preprocessed according to the image biomarker standardization initiative (IBSI) standards (using fixed 8-bin gray-level discretization). From localized 17 × 17pixel region of interest (ROI) patches, a highly compact 8-dimensional dual-domain feature vector is constructed. This vector is fed into a lightweight multilayer perceptron (MLP) for 3-class classification (Hemorrhagic vs. Ischemic vs. Normal), ensuring full feature-level explainability. Robustness is guaranteed using a strict patient-grouped 5-fold cross-validation protocol to prevent data leakage. Results The model achieved a peak diagnostic accuracy of 99.17% using clinical data from the Regional Hospital Center of Bafoussam, and maintained a high performance of 98.00% on the external Teknofest-2021 dataset. A comprehensive performance report table and a multi-class confusion matrix confirm the model's precision in distinguishing between tissue types. Furthermore, multi-class calibration curves demonstrate high diagnostic reliability and probability alignment, providing clinicians with explainable, well-calibrated confidence scores for decision-making. This framework is supported by a prototype interactive ROI-based diagnostic tool running efficiently on standard CPU hardware. Conclusion The integration of DWT and GLCM with MLP classification demonstrates exceptional robustness and high precision, surpassing several state-of-the-art methods in recent literature. Owing to its low computational footprint, this framework offers a straightforward implementation path into existing clinical workflows, providing an accessible and reliable CAD tool for stroke management in resource-constrained emergency settings.