[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126344-en":3,"doc-seo-126344-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126344,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","An approach for predicting brain tumor with machine learning techniques - Abstract and survey structure","The content presents a machine learning–driven approach for predicting brain tumor outcomes by leveraging MRI-based automated diagnosis. It motivates early detection to improve prognosis and reduce the burden of manual, slice-by-slice tumor extraction. The survey-like material reviews benchmark datasets, compares processing pipelines, and details feature extraction, segmentation, and classification methods. It also outlines the major challenges and the end-to-end steps from data acquisition to classification, aiming to support researchers and clinicians.","An approach for predicting brain tumor with machine learning  \ntechniques  \nPSRB Shashank1, L. Anand1, R. Pitchai2  \n1Department of Networking and Communications, College of Engineering and Technology (CET), SRM Institute of Science and  \nTechnology, Kattankulathur, Tamil Nadu, India  \n2Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, Telangana, India  \nArticle history:  \nReceived Aug 20, 2024 Revised Mar 29, 2025 Accepted May 24, 2025  \nKeywords:  \nBrain tumor classification Machine learning Magnetic resonance imaging Medical image processing Tumor segmentation  \nCorresponding Author:  \nThe medical industry relies heavily on image processing for tumor diagnosis. Medical imaging is an ever evolving and intricate field. Brain tumor (BT) is extremely frequent and may cause death. A BT develops when brain cells divide and grow out of control. The prognosis for people with BT can be greatly improved and the survival rate can be increased if the tumor is detected early. A single individual's brain magnetic resonance imaging (MRI) scan comprises multiple slices through the 3D anatomical perspective. As a result, extracting tumor from MRI scans is a difficult and time-consuming laborious task. Because of the risks associated with biopsies, an MRI-based automated BT categorization is a safer alternative. The scientific profession has worked tirelessly from the beginning of the millennium to develop an automatic BT segmentation and classification system. Therefore, there is a large body of work in the field dedicated to the study of BT research through machine learning (ML) techniques. The review paper summarizes the publicly accessible benchmark datasets typically used and compares various processing approaches, feature extraction (FE), segmentation, and classification algorithms for BT. The report also emphasizes the challenges of BT detection. Our hope is that this survey will provide researchers, clinicians, and other interested parties will gain an indepth understanding of BT segmentation and classification using ML.  \nThis is an open access article under the CC BY-SA license.  \nL. Anand  \nDepartment of Networking and Communications, College of Engineering and Technology (CET), SRM Institute of Science and Technology  \nChennai, India  \nEmail: [anand.l@srmist.edu.in](anand.l@srmist.edu.in)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nBrain tumor (BT) are masses of mutated cells that grow in the brain's tissues. BT can be either benign or malignant. Even in the absence of additional symptoms [1], [2], malignant BTs are among the deadliest types of cancer. On the other hand, benign BTs are curable through surgical removal. BTs can be classified as either primary or metastatic. Over 80% of malignant BTs in adults are gliomas, which originate in periglial tissue, followed by primary central nervous system (CNS) lymphomas. According to estimates from the Global Cancer Statistics 2020 [3], there will be around 1.6% and 2.5% of new cases and deaths. If BTs are found at an early stage, they can be effectively treated. Magnetic resonance imaging (MRI) is the gold standard because it provides high-quality images of both healthy and diseased tissues in a short amount of time [4] . Slice thickness, image quality, and inter-slice gap are all affected by the magnetic field strength and sampling methods [5] . In order to create an image, the scanner's built-in radio antenna must first take up the  \nsinusoidal signal [6] . BT are classified into various categories from slow growing to the most dangerous tumor types [7], [8] . The sample MRI from each category of BT is given in Figure 1. The first row represents the glioma type, the second shows the meningioma, the third gives the pituitary, and the fourth row is an example of a healthy brain. The automatic segmentation and classification of BT using MRI plays a vital role in the field of medicine. The survey aims to give a detailed description of th","cbCaiqraYEwR3L0s","https://ap.wps.com/l/cbCaiqraYEwR3L0s","pdf",917094,4,1,9,"English","en",105,"# Introduction\n## Brain tumor types and importance of early detection\n## MRI as the gold standard\n# Method\n## Data collection and acquisition\n## Image preprocessing\n## Tumor segmentation\n## Feature extraction\n## Classification with ML models\n# Survey chapters and overview\n## Public datasets\n## Segmentation methods\n## Feature extraction techniques\n## ML model families and algorithms\n## Challenges and conclusion","[{\"question\":\"为什么基于MRI的自动脑肿瘤分类更有价值？\",\"answer\":\"MRI能够提供高质量的健康与病变组织成像，并可在较短时间内完成采集。相比依赖活检的流程，自动化的MRI检测与分类被认为更安全，且能支持更早发现，从而改善预后。\"},{\"question\":\"脑肿瘤分割与分类的核心流程包含哪些步骤？\",\"answer\":\"流程包括数据采集、图像预处理以优化模型性能、利用分割定位肿瘤区域、特征提取以获得关键表征并降维，以及用机器学习模型完成二分类或多分类。\"},{\"question\":\"文中如何组织对现有研究与方法的回顾？\",\"answer\":\"材料按章节覆盖脑肿瘤与类型介绍、方法学与流程图、公开数据库、预处理、分割方法及其类型、基于颜色与形状的特征提取、机器学习模型类别与算法、分类与分割挑战，最后给出总结与结论。\"}]","An approach for predicting brain tumor with machine learning techniques - Abstract and survey structure | PDF",1785904580,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"an-approach-for-predicting-brain-tumor-with-machine-learning-techniques-abstract-and-survey-structure","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/an-approach-for-predicting-brain-tumor-with-machine-learning-techniques-abstract-and-survey-structure/126344/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"为什么基于MRI的自动脑肿瘤分类更有价值？","Question",{"text":76,"@type":77},"MRI能够提供高质量的健康与病变组织成像，并可在较短时间内完成采集。相比依赖活检的流程，自动化的MRI检测与分类被认为更安全，且能支持更早发现，从而改善预后。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"脑肿瘤分割与分类的核心流程包含哪些步骤？",{"text":81,"@type":77},"流程包括数据采集、图像预处理以优化模型性能、利用分割定位肿瘤区域、特征提取以获得关键表征并降维，以及用机器学习模型完成二分类或多分类。",{"name":83,"@type":74,"acceptedAnswer":84},"文中如何组织对现有研究与方法的回顾？",{"text":85,"@type":77},"材料按章节覆盖脑肿瘤与类型介绍、方法学与流程图、公开数据库、预处理、分割方法及其类型、基于颜色与形状的特征提取、机器学习模型类别与算法、分类与分割挑战，最后给出总结与结论。","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]