dc.contributor.advisor | Alam, Md. Ashraful | |
dc.contributor.author | Imamuzzaman, A.S.M. | |
dc.contributor.author | Sakline, Redwan Islam | |
dc.contributor.author | Junaed, Sayed Rafi | |
dc.contributor.author | Hossain, Mohammad Iqbal | |
dc.contributor.author | Das, Dipto | |
dc.date.accessioned | 2021-10-11T05:12:48Z | |
dc.date.available | 2021-10-11T05:12:48Z | |
dc.date.copyright | 2021 | |
dc.date.issued | 2021-06 | |
dc.identifier.other | ID 20141034 | |
dc.identifier.other | ID 16201013 | |
dc.identifier.other | ID 17101064 | |
dc.identifier.other | ID 17101279 | |
dc.identifier.other | ID 17101135 | |
dc.identifier.uri | http://hdl.handle.net/10361/15203 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (page 38-39). | |
dc.description.abstract | A brain tumor is a collection of abnormal cells growth in brain. It is a neurological
disease which causes great damage and affects other healthy cells of brain. It can be
cancerous or non-cancerous. Nowadays, people are more concern about their health
issues. So, in this thesis paper we will design and implement an efficient machine
learning approach to detect brain tumor from image data. Moreover, the proposed
model approaches VGG16 and ResNet50 architectural model of Convolutional Neu ral Network (CNN). Through this model a neurosurgeon can easily detect the brain
tumor of a patient with more efficiency. Our proposed model uses MRI images, and
we also make a comparison between the two architectures of CNN. | en_US |
dc.description.statementofresponsibility | A.S.M. Imamuzzaman | |
dc.description.statementofresponsibility | Redwan Islam Sakline | |
dc.description.statementofresponsibility | Sayed Rafi Junaed | |
dc.description.statementofresponsibility | Mohammad Iqbal Hossain | |
dc.description.statementofresponsibility | Dipto Das | |
dc.format.extent | 39 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac 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.subject | CNN | en_US |
dc.subject | VGG16 | en_US |
dc.subject | ResNet50 | en_US |
dc.subject | Brain Tumor | en_US |
dc.subject.lcsh | Brain Tumor | |
dc.title | Detecting brain tumor using deep neural networks from MRI images | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |