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dc.contributor.advisorAlam, Md. Ashraful
dc.contributor.advisorRodoshi, Ahanaf Hassan
dc.contributor.authorIslam, Annur Tasnim
dc.contributor.authorApu, Sakib Mashra
dc.contributor.authorSarker, Sudipta
dc.contributor.authorHasan, Inzamam M.
dc.contributor.authorShuvo, Syeed Alam
dc.date.accessioned2022-09-08T05:03:34Z
dc.date.available2022-09-08T05:03:34Z
dc.date.copyright2021
dc.date.issued2021-10
dc.identifier.otherID 18101021
dc.identifier.otherID 21141078
dc.identifier.otherID 18101631
dc.identifier.otherID 21241084
dc.identifier.otherID 21141080
dc.identifier.urihttp://hdl.handle.net/10361/17179
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 33-35).
dc.description.abstractA brain tumor is the development of mutated cells in the human brain. Many di er- ent types of brain tumors exist nowadays. According to researchers and physicians, some brain tumors are non-cancerous while some are life-threatening. In most cases, the cancer is detected at the last stage and it is di cult to recover. This increases the mortality rate. If this could be detected in the initial stages, then a lot more lives could be saved. Nowadays, brain tumors are being detected through auto- mated processes using arti cial intelligence algorithms and brain image data. In this research, we propose an e cient approach to detect brain tumors using Mag- netic Resonance Imaging (MRI) data and a deep neural network. The proposed system comprises several steps - preprocessing and classi cation of brain MRI im- ages. Furthermore, we analyzed the performances of di erent deep neural network architectures and optimized them with an e cient one. The proposed model en- ables classifying brain tumors e ectively with higher accuracy. To commence, we collected data and classi ed it using the ResNetl0l, ResNet50, InceptionV3, VGG19, and VGG19 architectures. As a consequence of our analysis, we obtained an accu- racy rate of 96.72% for VGG16, 96.17% for ResNet50, and 95.55% for InceptionV3. Then, using these three top classi ers, we constructed an ensemble model and ob- tained an overall accuracy rate of 98.60% using EBTDM (Explainable Brain Tumor Detection Model).en_US
dc.description.statementofresponsibilityAnnur Tasnim Islam
dc.description.statementofresponsibilitySakib Mashra Apu
dc.description.statementofresponsibilitySudipta Sarker
dc.description.statementofresponsibilityInzamam M. Hasan
dc.description.statementofresponsibilitySyeed Alam Shuvo
dc.format.extent35 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectBrain Tumoren_US
dc.subjectMRIen_US
dc.subjectDeep Neural Networken_US
dc.subjectVGGen_US
dc.subjectResNeten_US
dc.subjectEfficient- Neten_US
dc.subjectInceptionen_US
dc.subjectEnsembleen_US
dc.subjectEBTDMen_US
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshImage processing -- Digital techniques
dc.titleAn Efficient deep learning approach to detect Brain Tumor using MRI imagesen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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