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dc.contributor.advisorAlam, Md. Ashraful
dc.contributor.authorTuhin, Md. Akram Hossan
dc.contributor.authorPramanick, Tarunya
dc.contributor.authorEmon, Humayoun Kabir
dc.contributor.authorRahman, Wasiur
dc.date.accessioned2019-07-03T09:01:01Z
dc.date.available2019-07-03T09:01:01Z
dc.date.copyright2019
dc.date.issued2019-04
dc.identifier.otherID 14301079
dc.identifier.otherID 15101087
dc.identifier.otherID 18241051
dc.identifier.otherID 14301102
dc.identifier.urihttp://hdl.handle.net/10361/12301
dc.descriptionThis 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.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 41-43).
dc.description.abstractAmong 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.statementofresponsibilityMd. Akram Hossan Tuhin
dc.description.statementofresponsibilityTarunya Pramanick
dc.description.statementofresponsibilityHumayoun Kabir Emon
dc.description.statementofresponsibilityWasiur Rahman
dc.format.extent47 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.subject3D visualizationen_US
dc.subjectBrain Tumoren_US
dc.subjectDeep learningen_US
dc.subjectPolynomial interpolationen_US
dc.subjectOtsu's multithresholdingen_US
dc.subjectSegmentationen_US
dc.subject.lcshThree-dimensional imaging
dc.subject.lcshInformation visualization
dc.titleDetection and 3D visualization of Brain tumor using deep learning and polynomial interpolationen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science and Engineering


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