[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124324-en":3,"doc-seo-124324-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124324,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Segmentation Analysis for Brain Stroke Diagnosis Based on Susceptibility-Weighted Imaging (SWI) using Machine Learning - Automatic Stroke Segmentation and Classification Framework","Magnetic Resonance Imaging (MRI) supports the diagnosis of brain disorders, with stroke demanding rapid action because early intervention within six hours can reduce mortality and improve outcomes. Manual neuroradiologist assessment remains subjective and time-consuming, motivating an automatic pipeline for diagnosing and segmenting brain stroke using Susceptibility Weighted Imaging (SWI) and machine learning. The four-stage workflow includes pre-processing, segmentation, feature extraction, and classification. Pre-processing and segmentation identify stroke regions, evaluated by Jaccard, Dice, false positive, and false negative metrics. Adaptive threshold achieves the best lesion segmentation with a Dice coefficient of 0.96, enabling efficient CAD support for timely diagnosis.","Segmentation Analysis for Brain Stroke Diagnosis Based on Susceptibility-Weighted Imaging (SWI)  \nusing Machine Learning  \nShaarmila Kandaya 1, Abdul Rahim Abdullah2, Norhashimah Mohd Saad3, Ezreen Farina4, Ahmad Sobri Muda5  \nDepartment Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia 1, 2, 4  \nDepartment of Electrical and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka, Malaysia 3 Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia5  \nAbstract—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 timeconsuming. 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.  \nKeywords—Magnetic Resonance Imaging (MRI) diagnosis, time is brain, Susceptibility Weighted Imaging (SWI) and dice coefficient  \nI. INTRODUCTION  \nCerebrovascular accident (CVA) or stroke stands as the third leading cause of death in Malaysia [1] . This presents a significant challenge to the Malaysian healthcare system, witnessing over 50,000 new cases annually, resulting in at least 32 daily fatalities. In 2016, the government committed RM180 million to address this issue. Globally, stroke is the second leading cause of death, surpassed only by coronary artery disease, and it ranks prominently in causing long-term disability. The Malaysian National Stroke Association (NASAM) underscores the urgency of immediate medical attention for stroke, as swift treatment, especially within six hours, has been shown to save lives. However, the scarcity of neuroradiologists, with only 107 specialists, and the reliance on manual interpretation of magnetic resonance imaging (MRI) images hinder timely treatment efforts.  \nBrain stroke, characterized by a network of small blood vessels facilitating blood flow through a stroke or blocked artery, requires rapid and accurate diagnosis for prompt intervention. While MRI has gained preference over conventional angiography for diagnosing brain stroke, the current practice involves labor-intensive visual inspection, delaying the process [2] . Timely diagnosis and treatment are critical to preventing disability caused by insufficient blood and oxygen, leading to nerve cell death. Diagnostic considerations include factors like infarct volume, penumbra size, and the presence of adequate early stroke, all crucial for successful treatment [3] . Neuroradiologists urgently need efficient tools for quick and accurate acute stroke diagnosis.  \nMoreover, brain stroke detection from MRI images faces challenges due to noise, artifacts, vessel size, and structural heterogeneity [4] . Novel methods for segmenting and classifying medical im","cbCaiiAGmCwvEYqZ","https://ap.wps.com/l/cbCaiiAGmCwvEYqZ","pdf",877966,1,9,"English","en",105,"# Introduction\n## Stroke urgency and clinical context\n## MRI-based diagnosis challenges\n## Machine learning approaches and limitations\n# Literature Review\n## Human Brain","[{\"question\":\"Why is early stroke diagnosis considered critical?\",\"answer\":\"Early treatment within six hours can save lives and improve outcomes, reflected in the emphasis on “time is brain.” The document also links delays to disability and neuronal death.\"},{\"question\":\"What MRI sequence and data are used in the proposed approach?\",\"answer\":\"The method focuses on Susceptibility Weighted Imaging (SWI) sequences from MRI to support automatic stroke detection and segmentation.\"},{\"question\":\"How is the segmentation performance evaluated?\",\"answer\":\"Segmentation is assessed using Jaccard indices, Dice coefficient, false positive rate, and false negative rate.\"}]","Segmentation Analysis for Brain Stroke Diagnosis Based on Susceptibility-Weighted Imaging (SWI) using Machine Learning - Automatic Stroke Segmentation and Classification Framework | PDF",1785821605,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"segmentation-analysis-for-brain-stroke-diagnosis-based-on-susceptibility-weighted-imaging-swi-using-machine-learning-automatic-stroke-segmentation-and-classification-framework","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/segmentation-analysis-for-brain-stroke-diagnosis-based-on-susceptibility-weighted-imaging-swi-using-machine-learning-automatic-stroke-segmentation-and-classification-framework/124324/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early stroke diagnosis considered critical?","Question",{"text":75,"@type":76},"Early treatment within six hours can save lives and improve outcomes, reflected in the emphasis on “time is brain.” The document also links delays to disability and neuronal death.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What MRI sequence and data are used in the proposed approach?",{"text":80,"@type":76},"The method focuses on Susceptibility Weighted Imaging (SWI) sequences from MRI to support automatic stroke detection and segmentation.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the segmentation performance evaluated?",{"text":84,"@type":76},"Segmentation is assessed using Jaccard indices, Dice coefficient, false positive rate, and false negative rate.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]