[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117081-en":3,"doc-seo-117081-105":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},117081,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Brain Tumor Detection through Machine Learning Classification","Brain Tumor Detection through Machine Learning Classification evaluates machine learning performance for analyzing brain tumor MRI images to reduce medical professionals’ workload. The study compares multiple approaches for MRI data processing, including traditional algorithms (random forest, KNN, SVM) and convolutional neural networks (Custom CNN, ResNet v2, VGG 16). Results indicate CNN models achieve higher tumor classification accuracy than traditional methods. VGG 16 delivers the top accuracy of 98.73% with the lowest loss among CNN variants, supporting model comparison across different tumor image types.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nBrain Tumor Detection through Machine Learning Classification  \nPermalink  \n[https://escholarship.org/uc/item/60q331ck](https://escholarship.org/uc/item/60q331ck)  \nAuthor  \nLing, Long  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA Los Angeles  \nBrain Tumor Detection through Machine Learning Classification  \nA thesis submitted in partial satisfaction of the requirements for the degree Master of Applied Statistics and Data Science  \nby  \nLong Ling  \n2024  \n© Copyright by Long Ling 2024  \nABSTRACT OF THE THESIS  \nBrain Tumor Detection through Machine Learning Classification  \nby  \nLong Ling  \nMaster of Applied Statistics and Data Science  \nUniversity of California, Los Angeles, 2024  \nProfessor Yingnian Wu, Chair  \nThis article examines the efficacy of machine learning techniques in analyzing brain tumor MRI images with the aim of reducing the workload of medical professionals. The study compared various machine learning methods for processing MRI data and their accuracy. Results show that convolutional neural networks (CNN), including Custom CNN, ResNet v2, and VGG 16, outperform traditional machine learning algorithms such as random forest, KNN, and SVM in tumor classification accuracy. VGG 16 shows the highest accuracy, reaching 98.73%, and has the smallest loss compared to other CNN models. These data results provide insights into the comparative performance of machine learning models, revealing their strengths and limitations in processing different brain tumor images.  \nThe thesis of Long Ling is approved.  \nMichael Tsiang Frederic R. Paik Schoenberg Yingnian Wu, Committee Chair  \nUniversity of California, Los Angeles 2024  \nTABLE OF CONTENTS  \n1 Introduction ...................................... 1  \n2 Data Introduction .................................. 4  \n2.1 Data Preprocessing ................................ 4  \n3 Methodology and Models ............................. 7  \n3.1 Random Forest .................................. 7  \n3.2 KNN ........................................ 10  \n3.3 SVM ........................................ 12  \n3.4 CNN ........................................ 16  \n3.4.1 Custom CNN ............................... 17  \n3.4.2 CNN ResNet v2 .............................. 21  \n3.4.3 CNN VGG 16 ............................... 24  \n4 Discussion ....................................... 28  \nReferences ......................................... 30  \nLIST OF FIGURES  \n2.1 Class Distribution in Training and Test Sets .................... 5  \n2.2 Glioma Tumor Sample ................................ 5  \n2.3 Meningioma Tumor Sample ............................. 6  \n2.4 No Tumor Sample .................................. 6  \n2.5 Pituitary Tumor Sample ............................... 6  \n3.1 Random Forest Classifier [7] ............................. 8  \n3.2 Random Forest Confusion Matrix .......................... 9  \n3.3 K-Nearest Neighbors Classifier [11] ......................... 10  \n3.4 k-NN Confusion Matrix ............................... 12  \n3.5 SVM Classifier .................................... 14  \n3.6 SVM Confusion Matrix ................................ 15  \n3.7 Custom CNN Accuracy ............................... 18  \n3.8 Custom CNN Loss .................................. 19  \n3.9 ResNet v2 Accuracy ................................. 22  \n3.10 ResNet v2 Loss .................................... 23  \n3.11 VGG 16 Accuracy .................................. 25  \n3.12 VGG 16 Loss ..................................... 26  \nLIST OF TABLES  \n2.1 Class Distribution ................................... 4  \n3.1 Random Forest Classification Report ........................ 9  \n3.2 k-NN Classification Report .............................. 11  \n3.3 SVM Classification Report .............................. 15  \n3.4 ","cbCaisA7GBy7612j","https://ap.wps.com/l/cbCaisA7GBy7612j","pdf",2216248,1,39,"English","en",105,"# Introduction\n# Data Introduction\n## Data Preprocessing\n# Methodology and Models\n## Random Forest\n## KNN\n## SVM\n## CNN\n## Custom CNN\n## CNN ResNet v2\n## CNN VGG 16\n# Discussion\n# References","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To evaluate how machine learning techniques can analyze brain tumor MRI images and reduce the workload of medical professionals.\"},{\"question\":\"Which model performs best in tumor classification accuracy?\",\"answer\":\"VGG 16 shows the highest accuracy, reaching 98.73%, and also has the smallest loss among the compared CNN models.\"},{\"question\":\"How do CNN methods compare with traditional machine learning algorithms?\",\"answer\":\"CNN models, including Custom CNN, ResNet v2, and VGG 16, outperform traditional methods such as random forest, KNN, and SVM in tumor classification accuracy.\"}]","Brain Tumor Detection through Machine Learning Classification | PDF",1785673613,98,{"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},"brain-tumor-detection-through-machine-learning-classification","",{"@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/brain-tumor-detection-through-machine-learning-classification/117081/",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","2026-08-02",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},"What is the main goal of the thesis?","Question",{"text":75,"@type":76},"To evaluate how machine learning techniques can analyze brain tumor MRI images and reduce the workload of medical professionals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which model performs best in tumor classification accuracy?",{"text":80,"@type":76},"VGG 16 shows the highest accuracy, reaching 98.73%, and also has the smallest loss among the compared CNN models.",{"name":82,"@type":73,"acceptedAnswer":83},"How do CNN methods compare with traditional machine learning algorithms?",{"text":84,"@type":76},"CNN models, including Custom CNN, ResNet v2, and VGG 16, outperform traditional methods such as random forest, KNN, and SVM in tumor classification accuracy.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]