dc.contributor.advisor | Alam, Md. Ashraful | |
dc.contributor.author | Tuhin, Md. Akram Hossan | |
dc.contributor.author | Pramanick, Tarunya | |
dc.contributor.author | Emon, Humayoun Kabir | |
dc.contributor.author | Rahman, Wasiur | |
dc.date.accessioned | 2019-07-03T09:01:01Z | |
dc.date.available | 2019-07-03T09:01:01Z | |
dc.date.copyright | 2019 | |
dc.date.issued | 2019-04 | |
dc.identifier.other | ID 14301079 | |
dc.identifier.other | ID 15101087 | |
dc.identifier.other | ID 18241051 | |
dc.identifier.other | ID 14301102 | |
dc.identifier.uri | http://hdl.handle.net/10361/12301 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 41-43). | |
dc.description.abstract | Among di erent imaging techniques MRI, MRSI and CT scans are some of the
widely use techniques to visualize brain structures to point out brain anomalies especially
brain tumor. Identi cation of brain tumor accurately in clinical practices
has always been a hard decision for neurologist as multiple exceptions might present
in images which may lead dubious suggestion from neurologist.In our proposed model
we are aiming towards brain tumor detection and 3d visualization of tumor more
accurately in e cient way. Our proposed model composed of three stages such as
classi cation of image using CNN whether any tumor exists of not; segmentation
using multi thresholding to extract the detected tumor; and 3d visualization using
polynomial interpolation. the proposed model enables enhancing the accuracy of
tumor detection as compare to existing models as well as segmenting and 3d visualizing
the detected tumor. we get 85% accuracy on our model comparing with others
which is slightly more e cient in terms of classi cation and detection. | en_US |
dc.description.statementofresponsibility | Md. Akram Hossan Tuhin | |
dc.description.statementofresponsibility | Tarunya Pramanick | |
dc.description.statementofresponsibility | Humayoun Kabir Emon | |
dc.description.statementofresponsibility | Wasiur Rahman | |
dc.format.extent | 47 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 | 3D visualization | en_US |
dc.subject | Brain Tumor | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Polynomial interpolation | en_US |
dc.subject | Otsu's multithresholding | en_US |
dc.subject | Segmentation | en_US |
dc.subject.lcsh | Three-dimensional imaging | |
dc.subject.lcsh | Information visualization | |
dc.title | Detection and 3D visualization of Brain tumor using deep learning and polynomial interpolation | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science and Engineering | |