Intracranial Hemorrhage (ICH) represents a life-threatening neurological condition that demands immediate and precise diagnosis for timely clinical intervention. However, most existing automated detection systems struggle with issues such as unbalanced datasets, poor adaptability across data sources, and insufficient attention to hemorrhage-specific brain regions. To overcome these limitations, this study introduces a comprehensive dataset for ICH detection that integrates newly acquired hospital images with existing open-access datasets, encompassing six major subtypes of hemorrhage. A novel two-stage ensemble framework is proposed: in the first stage, image quality is enhanced using Contrast Limited Adaptive Histogram Equalization (CLAHE); in the second stage, a Brain Region Detector based on You Only Look Once version 11 (YOLOv11) is employed to accurately localize potential hemorrhagic regions. From these localized areas, Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) features are extracted, fused, and normalized through Min–Max scaling. The feature space is then optimized using Linear Discriminant Analysis (LDA) to enhance class separability. Finally, a Support Vector Machine (SVM) classifier with a Radial Basis Function (RBF) kernel is used for classification, achieving a 96.13% accuracy rate, surpassing both traditional and deep learning-based approaches. The proposed model delivers a practical balance between interpretability, computational efficiency, and diagnostic accuracy, offering a new benchmark for real-time multi-class ICH detection in clinical environments.