Brain image fMRI data classification and graphical representation of visual object
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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| Lenguaje: | English |
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Brac University
2019
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| Acceso en línea: | http://hdl.handle.net/10361/12349 |
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10361-12349 |
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10361-123492022-01-26T10:10:22Z Brain image fMRI data classification and graphical representation of visual object Tasneem, Nazifa Afroza Tithi, Indrani Datta Shuchi, Ummay Sadia Khanum Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Brain image fMRI data Visual object Neural networks This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 43-44). Analyzing neuroimaging data has become a research of interest these days because of their several applications starting from analysis of brain region connectivity to analysis of ventral streams and visual stimuli. In this paper, we propose a model that explains what image a human brain visually perceives based on the neuroimaging information from the ventral temporal cortex (VT) portion. In the model, we used the nilearn library from python repository along with the haxby data set which includes a set of functional MRI from 6 subjects viewing images that contains a grid of black and white pictures of some certain figures. Firstly, the haxby data set was collected and few pre-processing steps such as masking, scaling and smoothing was done in order to reduce the complexity, noise and to standardize the data. Then, the entire data set was cross validated into 80 percent of training example and 20 percent of test example. After the splitting was done, the training examples were passed through a set of learning frameworks such as ‘Nearest Neighbors’, ‘Linear SVM’, ‘RBF SVM’, ‘Gaussian Process’, ‘Decision Tree’, ‘Random Forest’, ‘Neural Net’, ‘AdaBoost’, ‘Naive Bayes’ and ‘QDA’ algorithms. Completing the training, the accuracy of the frameworks was tested and on an average the most accuracy of 95 percent was found with Neural Network and Support Vector Machine (SVM) across all the subjects. Indrani Datta Tithi Ummay Sadia Khanum Shuchi Nazifa Afroza Tasneem B. Computer Science and Engineering 2019-07-11T07:42:38Z 2019-07-11T07:42:38Z 2018 2018-12 Thesis ID 18241028 ID 18241030 ID 16101121 http://hdl.handle.net/10361/12349 en 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. 44 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Brain image fMRI data Visual object Neural networks |
| spellingShingle |
Brain image fMRI data Visual object Neural networks Tasneem, Nazifa Afroza Tithi, Indrani Datta Shuchi, Ummay Sadia Khanum Brain image fMRI data classification and graphical representation of visual object |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Tasneem, Nazifa Afroza Tithi, Indrani Datta Shuchi, Ummay Sadia Khanum |
| format |
Thesis |
| author |
Tasneem, Nazifa Afroza Tithi, Indrani Datta Shuchi, Ummay Sadia Khanum |
| author_sort |
Tasneem, Nazifa Afroza |
| title |
Brain image fMRI data classification and graphical representation of visual object |
| title_short |
Brain image fMRI data classification and graphical representation of visual object |
| title_full |
Brain image fMRI data classification and graphical representation of visual object |
| title_fullStr |
Brain image fMRI data classification and graphical representation of visual object |
| title_full_unstemmed |
Brain image fMRI data classification and graphical representation of visual object |
| title_sort |
brain image fmri data classification and graphical representation of visual object |
| publisher |
Brac University |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10361/12349 |
| work_keys_str_mv |
AT tasneemnazifaafroza brainimagefmridataclassificationandgraphicalrepresentationofvisualobject AT tithiindranidatta brainimagefmridataclassificationandgraphicalrepresentationofvisualobject AT shuchiummaysadiakhanum brainimagefmridataclassificationandgraphicalrepresentationofvisualobject |
| _version_ |
1814307632813441024 |