[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122858-en":3,"doc-seo-122858-105":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},122858,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",7,"Healthcare","STUDY OF BRAIN TUMOR PREDICTION BY USING MACHINE LEARNING","Advances in deep learning and machine learning have strengthened the diagnosis and analysis of medical images. This culminating project applies the EfficientNetV2B3 model to brain tumor prediction and evaluates three research questions: whether the proposed deep learning approach improves over existing methods, how model accuracy varies with different optimizers (Adagrad, Adam, SGD), and whether regularization such as dropout enhances generalization. Results confirm improved performance, with Adam achieving the highest accuracy, and dropout raising performance from 98% to 99%.","California State University, San Bernardino  \nCSUSB ScholarWorks  \n\n| Electronic Theses, Projects, and Dissertations | Office of Graduate Studies |\n| --- | --- |\n| 5-2024\u003Cbr>STUDY OF BRAIN TUMOR PREDICTION BY USING MACHINE LEARNING\u003Cbr>Vishaya Ummaneni\u003Cbr>Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)\u003Cbr> Part of the Business Intelligence Commons |  |\n\nRecommended Citation  \nUmmaneni, Vishaya, \"STUDY OF BRAIN TUMOR PREDICTION BY USING MACHINE LEARNING\" (2024) . Electronic Theses, Projects, and Dissertations. 1965.  \n[https://scholarworks.lib.csusb.edu/etd/1965](https://scholarworks.lib.csusb.edu/etd/1965)  \nThis Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [scholarworks@csusb.edu](scholarworks@csusb.edu).  \nSTUDY OF BRAIN TUMOR PREDICTION BY USING MACHINE LEARNING  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nIn Partial Fulfillment  \nof the Requirements for the Degree Master of Science  \nin  \nInformation Systems and Technology  \nby Vishaya Ummaneni  \nMay 2024  \nSTUDY OF BRAIN TUMOR PREDICTION BY USING MACHINE LEARNING  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby Vishaya Ummaneni May 2024 Approved by:  \nDr. Conrad Shayo , Committee Member, Chair  \nDr. Nima Molavi, Committee Member, Reader  \nDr. Barbara Sirotnik, Committee Member, Reader  \n© 2024 Vishaya Ummaneni  \nABSTRACT  \nTechnological advancements in deep learning and machine learning have greatly improved the diagnosis and analysis of medical images. This culminating experience project utilized the EfficientNetV2B3 model to predict brain tumors. The research questions are: (Q1) Does the study's deep learning model perform better than current methods when it comes to predicting brain tumor? (Q2) How much does the model's performance change when using different optimizers such as Adagrad , Adam , and SGD? (Q3) Can the regularization method , such as dropout , enhance the neural network model's generalization? The findings are as follows: (Q1) Yes; the EfficientNetV2B3 model performs better than current methods. (Q2) Based on optimizer value , accuracy is varied: the Adam optimizer provides a higher performance compared to Adagrad and SGD optimizers. (Q3)(a ) Yes, using regularization methods helps improve model generalization; (b) model performance is improved from 98% to 99% after using the dropout layer. Finally, the conclusion is that the EfficientNetV2B3 model performs well on brain tumor prediction.  \nACKNOWLEDGEMENTS  \nThe completion of this project journey has been marked by challenges, growth , and invaluable lessons. I am filled with so much gratitude to everyone who contributed to its comprehension. I would like to sincerely thank my committee chair cum advisor, Dr. Shayo whose continuous guidance, expertise and patience have been essential throughout this endeavor. Their mentorship not only shaped the focus of this research , but also helped me to become a better person and scholar. I’m indebted to my Principal Investigators, Dr. Molavi and Dr. Sirotnik, for the thoughtful feedback, helpful criticism , and ideas that have madea huge difference in the way I do this project. Furthermore , I would like to let everyone know that I appreciate every participant who shared their integral experience with us. They gave me access and support that no other person had. This would not have been possible without them. As they were major figures in the research , they greatly impacted and stabilized the study outcomes. Alongside , I feel indebted to my family that did not drop their steadfast assistance , love , and motivation. My mother’s faith in me acted as a continuous undying motivato","cbCaibs07qlnsdDE","https://ap.wps.com/l/cbCaibs07qlnsdDE","pdf",1119792,1,61,"English","en",105,"# ABSTRACT\n# ACKNOWLEDGEMENTS\n# LIST OF TABLES\n# LIST OF FIGURES\n# CHAPTER ONE: INTRODUCTION\n## Problem Statement\n## Research Questions\n## Organization of the Study\n# CHAPTER TWO: LITERATURE REVIEW\n# CHAPTER THREE: RESEARCH METHODS","[{\"question\":\"Which model is used for brain tumor prediction in this project?\",\"answer\":\"The project uses the EfficientNetV2B3 deep learning model for brain tumor prediction.\"},{\"question\":\"How do different optimizers affect model performance?\",\"answer\":\"Accuracy varies by optimizer; the Adam optimizer provides higher performance than Adagrad and SGD.\"},{\"question\":\"Does regularization with dropout improve generalization?\",\"answer\":\"Yes. Using regularization methods, including a dropout layer, improves generalization and increases performance from 98% to 99%.\"}]","STUDY OF BRAIN TUMOR PREDICTION BY USING MACHINE LEARNING | PDF",1785813355,154,{"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},"study-of-brain-tumor-prediction-by-using-machine-learning","",{"@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/study-of-brain-tumor-prediction-by-using-machine-learning/122858/",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},"Which model is used for brain tumor prediction in this project?","Question",{"text":75,"@type":76},"The project uses the EfficientNetV2B3 deep learning model for brain tumor prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do different optimizers affect model performance?",{"text":80,"@type":76},"Accuracy varies by optimizer; the Adam optimizer provides higher performance than Adagrad and SGD.",{"name":82,"@type":73,"acceptedAnswer":83},"Does regularization with dropout improve generalization?",{"text":84,"@type":76},"Yes. 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