Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches

Cataloged from PDF version of thesis.

Sonraí bibleagrafaíochta
Príomhchruthaitheoirí:	Shahriar, Farhan, Dey, Amarttya Prasad, Rahman, Naimur, Tasnim, Zarin, Tanvir, Mohammad Zubayer
Rannpháirtithe:	Parvez, Zavid
Formáid:	Tráchtas
Teanga:	English
Foilsithe / Cruthaithe:	Brac University 2021
Ábhair:	Convolutional Neural Network (CNN) Parkinson’s Disease (PD) Neural Network (NN) fMRI Deep Learning Average Ensemble VGG19 Inception-ResNet-v2 Inception-V3 MobileNet-V1 PPMI
Rochtain ar líne:	http://hdl.handle.net/10361/14999

id	10361-14999
record_format	dspace
spelling	10361-149992022-01-26T10:05:00Z Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches Shahriar, Farhan Dey, Amarttya Prasad Rahman, Naimur Tasnim, Zarin Tanvir, Mohammad Zubayer Parvez, Zavid Department of Computer Science and Engineering, Brac University Convolutional Neural Network (CNN) Parkinson’s Disease (PD) Neural Network (NN) fMRI Deep Learning Average Ensemble VGG19 Inception-ResNet-v2 Inception-V3 MobileNet-V1 PPMI Deep Learning Cataloged from PDF version of thesis. Includes bibliographical references (page 36-38). Parkinson’s Disease is the second most common neurological disease after Alzheimer’s Disease. The disease is incurable. However, if the disease can be detected earlier, then the consequences of it’s effect can be relieved. The early phase of PD is called by Prodromal Parkinson’s Disease. The symptoms of the Prodromal phase includes hyposmia, constipation, mood disorders, REM sleep behavior disorder, olfaction dis orders etc. RBD or REM sleep behavior disorder is the most common symptom of Prodromal PD. In this study, we used various deep convolutional neural network architectures and trained them to detect Prodromal PD patients. We collected 20 Prodromal patients and 20 healthy control subject data from the PPMI website and applied CNN architecture mobilenet v1, incception v3, vgg19 and inception resnet v2 to achieve our goal. We ensembled inception resnet v2 and mobilenet v1 with the hope of getting a better result as well. However, we successfully carried out our training and with mobilenet v1 we gained the highest classification accuracy of 81.22%. Inception resnet V2, inception v3, vgg19 and ensemble model achieved respectively 75.30%, 62.55% and 63.32% accuracy. Farhan Shahriar Amarttya Prasad Dey Naimur Rahman Mohammad Zubayer Tanvir Zarin Tasnim This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. B. Computer Science 2021-09-14T05:33:08Z 2021-09-14T05:33:08Z 2021 2021-06 Thesis ID: 14201046 ID: 16201081 ID: 15321002 ID: 13101154 ID: 13321045 http://hdl.handle.net/10361/14999 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. 38 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Convolutional Neural Network (CNN) Parkinson’s Disease (PD) Neural Network (NN) fMRI Deep Learning Average Ensemble VGG19 Inception-ResNet-v2 Inception-V3 MobileNet-V1 PPMI Deep Learning
spellingShingle	Convolutional Neural Network (CNN) Parkinson’s Disease (PD) Neural Network (NN) fMRI Deep Learning Average Ensemble VGG19 Inception-ResNet-v2 Inception-V3 MobileNet-V1 PPMI Deep Learning Shahriar, Farhan Dey, Amarttya Prasad Rahman, Naimur Tasnim, Zarin Tanvir, Mohammad Zubayer Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches
description	Cataloged from PDF version of thesis.
author2	Parvez, Zavid
author_facet	Parvez, Zavid Shahriar, Farhan Dey, Amarttya Prasad Rahman, Naimur Tasnim, Zarin Tanvir, Mohammad Zubayer
format	Thesis
author	Shahriar, Farhan Dey, Amarttya Prasad Rahman, Naimur Tasnim, Zarin Tanvir, Mohammad Zubayer
author_sort	Shahriar, Farhan
title	Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches
title_short	Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches
title_full	Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches
title_fullStr	Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches
title_full_unstemmed	Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches
title_sort	detection of prodromal parkinson’s disease with fmri data and deep neural network approaches
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/14999
work_keys_str_mv	AT shahriarfarhan detectionofprodromalparkinsonsdiseasewithfmridataanddeepneuralnetworkapproaches AT deyamarttyaprasad detectionofprodromalparkinsonsdiseasewithfmridataanddeepneuralnetworkapproaches AT rahmannaimur detectionofprodromalparkinsonsdiseasewithfmridataanddeepneuralnetworkapproaches AT tasnimzarin detectionofprodromalparkinsonsdiseasewithfmridataanddeepneuralnetworkapproaches AT tanvirmohammadzubayer detectionofprodromalparkinsonsdiseasewithfmridataanddeepneuralnetworkapproaches
_version_	1814307131040464896

Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches

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