Segmentation Analysis for Brain Stroke Diagnosis Based on Susceptibility-Weighted Imaging (SWI) using Machine Learning

Magnetic Resonance Imaging (MRI) plays a crucial role in diagnosing brain disorders, with stroke being a significant category among them. Recent studies emphasize the importance of swift treatment for stroke, known as "time is brain," as early intervention within six hours of stroke onset can save lives and improve outcomes. However, the conventional manual diagnosis of brain stroke by neuroradiologists is subjective and time-consuming. To address this issue, this study presents an automatic technique for diagnosing and segmenting brain stroke from MRI images according to pre and post stroke patient. The technique utilizes machine learning methods, focusing on Susceptibility Weighted Imaging (SWI) sequences. The machine learning technique involves four stage, those are pre-processing, segmentation, feature extraction, and classification. In this paper, preprocessing and segmentation are proposed to identify the stroke region. The segmentation performance is assessed using Jaccard indices, Dice Coefficient, false positive, and false negative rates. The results show that adaptive threshold performs best for stroke lesion segmentation, with good improvement stroke patient that achieving the highest Dice coefficient of 0.96. In conclusion, this proposed stroke segmentation technique has promising potential for diagnosing early brain stroke, providing an efficient and automated approach to aid medical professionals in timely and accurate diagnoses.