A Machine Learning Framework for Stroke Identification from Neuroimages

Stroke diagnosis is a time-critical medical task that requires rapid and precise identification to initiate appropriate treatment and prevent severe neurological damage. This study introduces a machine learning–driven diagnostic framework designed to identify stroke using neuroimaging data. A comprehensive set of machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest, Decision Tree, and Convolutional Neural Network (CNN)—were trained and evaluated on a curated dataset of brain images to classify stroke and non-stroke cases. The proposed system leverages the strengths of both classical and deep learning algorithms. Logistic Regression enables examination of relationships between neuroimage features and stroke presence, while SVM effectively separates complex feature patterns. Random Forest and Decision Tree models provide structured rule-based classification derived from key imaging attributes. The inclusion of a CNN significantly enhances diagnostic accuracy by extracting high-level spatial and textural features from neuroimages without manual feature engineering. Experimental results indicate that the CNN model outperforms all other techniques, achieving 95.6% accuracy, 94.2% sensitivity, and 96.5% specificity. Random Forest and SVM also show strong performance with accuracies of 93.1% and 92.5%, respectively. The findings confirm the potential of machine learning, particularly deep learning, in improving stroke diagnosis and supporting clinicians with faster and more reliable decision-making. By harnessing modern computational intelligence, this framework contributes toward reducing diagnostic delays, enhancing patient outcomes, and lowering the overall healthcare burden associated with stroke.