Quantum SVM-driven framework for accurate brain stroke classification.

A brain stroke is a critical cerebrovascular disorder that disrupts blood flow to specific brain regions, leading to irreversible neuronal damage if not diagnosed promptly. Accurate and early classification of stroke subtype plays a vital role in reducing mortality and improving recovery outcomes. Traditional diagnostic methods such as MRI and CT imaging rely heavily on manual interpretation, which can be time-consuming and prone to subjective variability. This research presents a Quantum support vector machine (QSVM) framework for automated brain stroke classification. The proposed system integrates a unified classical feature extraction pipeline comprising textural, morphological, frequency-domain, and statistical descriptors, followed by quantum state encoding within a six-qubit circuit using a ZZFeatureMap-based quantum kernel. The quantum kernel maps classical features into a higher-dimensional Hilbert space to enhance nonlinear separability. Experimental evaluation on a publicly available Kaggle MRI dataset using stratified 5-fold cross-validation demonstrates that the QSVM achieves 96.8% classification accuracy, 96.2% precision, 97.1% recall, F1-score of 96.6%, and an AUC-ROC of 0.982, outperforming optimized classical baselines including Random Forest, K-Nearest Neighbors, and traditional SVM variants on identical feature sets. All experiments were conducted using a classical quantum simulator; therefore, the reported improvements represent simulator-based performance gains rather than hardware-level quantum advantage. These findings suggest that quantum-inspired kernel methods can improve classification performance under controlled experimental conditions, warranting further validation on larger multicenter datasets and real quantum hardware.