Feature-based brain ischemic stroke detection in the fractional Hermite transform domain

Ischemic stroke, resulting from a blockage in cerebral blood flow, is a major global health concern due to its high rates of mortality and long-term disability. A fast and accurate diagnosis is essential for effective treatment and improved prognosis that allows to preserve the life of patients. Magnetic Resonance Imaging (MRI) is considered the standard image modality for stroke evaluation due to its sensitivity in detecting the ischemic lesions and the ability to provide detailed visualization of brain tissue abnormalities. In this work, we propose an automated system for ischemic stroke detection in MRI scans using the fractional Hermite transform (FrHT), a technique that captures localized spatial-frequency features. The methodology involves a preprocessing stage of the MRI slices, the FrHT-based features extraction, and the classification of stroke-affected regions using a supervised learning model. The system was evaluated on the publicly available ISLES dataset, which includes annotated MRI scans of stroke patients. Experimental results demonstrate the effectiveness of the proposed approach.