[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122160-en":3,"doc-seo-122160-105":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},122160,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine Learning-based Brain Tumor Segmentation - A Comprehensive Review","Brain tumor segmentation is a critical task in medical image processing, yet reliable classification and segmentation remain challenging due to tumor variability and the complexity of MRI data. Hospitals increasingly rely on machine learning and deep learning to improve efficiency in analysis and detection, supporting faster clinical recovery planning. This comprehensive review surveys recent ML/DL approaches for diagnosing and grouping brain illnesses from MRI images, discussing over 60 studies and key aspects such as network architecture and segmentation methods.","|  | \u003Cbr>JOURNAL`S UNIVERSITY OF BABYLON FOR ENGINEERING SCIENCES (JUBES)\u003Cbr>مــــــجلــة جـــــــامعة بـــــــــابــل للعلــــــــوم الهندسية |  |\n| --- | --- | --- |\n| Vol. 32, No. 4. \\ 2024 ISSN: 2616-9916 |  |  |\n\nMachine Learning-based Brain Tumor Segmentation: A Comprehensive  \nReview  \nRula Sami Aleesa1 Noora kadhim Al-bermani2 Hayder A. Ismael3 Thamir R. Saeed4  \n1,2 Faculty of Materials Engineering, University of Babylon, Babylon, Iraq  \n[mat.rula.sami@uobabylon.edu.iq](mat.rula.sami@uobabylon.edu.iq)  [mat.noorakadhim@uobabylon.edu.iq](mat.noorakadhim@uobabylon.edu.iq)  \n3 Biomedical Engineering Department,Al-Khwarizmi College of Engineering, University of  \nBaghdad, Baghdad, Iraq  \n[hayder.a@kecbu.uobaghdad.edu.iq](hayder.a@kecbu.uobaghdad.edu.iq)  \n4 Electrical Eng. Department, University of Technology, Iraq  \n[Thamir_rashed@yahoo.com](Thamir_rashed@yahoo.com)  \nAbstract  \nSegmentation of Brain tumors refers to a crucial function in medical image processing. Despite a lot of main attempts and satisfactory results in such a field, appropriate classification and segmentation remain an important function. Segmentation of an image is a hard task in the processing of an image. In order to improve the efficiency of processing, analysis, and detection, hospitals have already begun to use machine learning (ML) . To increase the speed of the recovery process started, doctors could get help with detection. In the last few years, techniques of machine learning have illustrated satisfactory performance in solving different issues of computer vision like semantic segmentation, image classification as well as object diagnosis. Several ML-based methods have been successfully applied to the problem of brain tumor segmentation. The present paper shows an overview of recent ML and deep learning techniques to diagnose and group brain illnesses from MRI images. More than 60 scientific studies are chosen and discussed here, covering technical features like network architecture design, and segmentation.  \nKeywords: Brain tumor segmentation, Classification, Deep learning, Machine Learning, Region Growing.  \n1. Introduction  \n\n| Received: | 14/4/2024 | Accepted: | 9/6/2024 | Published: | 15/8/2024 |\n| --- | --- | --- | --- | --- | --- |\n\nThe human brain is where all of the body's controls are located, it is the nervous system's important element that contains the spinal cord and wide nerves and neurons system. Everything in the body, from the senses to the muscles, is monitored by the nervous system. A lot of things could go wrong when the brain is damaged such as personality, memory, and sensation. Each ailment/disability influencing your brain is considered a brain disorder. Brain disorders contain each situation/disorder that influences the brain. They contain genetics, diseases, and traumarelated issues. There are various brain diseases’ kinds such as brain injuries induced by forceful  \n\n|  | \u003Cbr>JOURNAL`S UNIVERSITY OF BABYLON FOR ENGINEERING SCIENCES (JUBES)\u003Cbr>مــــــجلــة جـــــــامعة بـــــــــابــل للعلــــــــوم الهندسية |  |\n| --- | --- | --- |\n| Vol. 32, No. 4. \\ 2024 ISSN: 2616-9916 |  |  |\n\ntrauma. Brain tissue, nerves as well as neurons could be harmed by trauma [1] . Modalities of Brain imaging like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have an important role in brain abnormalities detection. The majority of CNS illnesses can be diagnosed with brain MRI, a noninvasive diagnostic test. General practitioners (GPs) may view the brain in large slices thanks to brain MRI. Magnetic Resonance Imaging (MRI) is the most precise imaging technique for identifying central nervous system (CNS) diseases and providing valuable information to educate patients about the results. Better picture contrast and real-time brain structure recognition during scanning are two advantages of MRI over CT. Medical experts can read medical imaging papers with ease and have a broad interest in them. With its many imaging orders ","cbCailllqeoknojc","https://ap.wps.com/l/cbCailllqeoknojc","pdf",1708333,1,26,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is brain tumor segmentation important in medical image processing?\",\"answer\":\"Brain tumor segmentation is essential for accurately identifying and isolating tumor regions of interest, which supports diagnosis and further clinical steps. It helps link imaging findings with treatment planning and monitoring.\"},{\"question\":\"What imaging modality is mainly used for brain tumor segmentation in this review?\",\"answer\":\"The review emphasizes MRI images as the primary source for applying machine learning and deep learning methods to diagnose and group brain illnesses.\"},{\"question\":\"What topics does the paper cover across the surveyed studies?\",\"answer\":\"The paper overviews recent ML and deep learning techniques and discusses more than 60 studies, including technical features such as network architecture design and segmentation approaches.\"}]","Machine Learning-based Brain Tumor Segmentation - A Comprehensive Review | PDF",1785809128,66,{"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},"machine-learning-based-brain-tumor-segmentation-a-comprehensive-review","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-brain-tumor-segmentation-a-comprehensive-review/122160/",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 brain tumor segmentation important in medical image processing?","Question",{"text":75,"@type":76},"Brain tumor segmentation is essential for accurately identifying and isolating tumor regions of interest, which supports diagnosis and further clinical steps. 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