Detection of Parkinson’s disease from Neuro-imagery using deep neural network with transfer learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
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Brac University
2021
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| Online Erişim: | http://hdl.handle.net/10361/14457 |
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10361-144572022-01-26T10:10:24Z Detection of Parkinson’s disease from Neuro-imagery using deep neural network with transfer learning Asaduzzaman Sakib, A.F.M. Nazmus Shusmita, Sanjida Ali Kabir, S. M. Ashraf Parvez, Mohammad Zavid Reza, Md. Tanzim Department of Computer Science and Engineering, Brac University Parkinson’s Disease (PD) Neurological condition Dopaminergic neurons PPMI MRI CNN Extract features VGG3 VGG16 VGG19 This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 54-59). Parkinson’s disease is a neurological condition that is dynamic and steadily influences the movement of the human body. It causes issues within the brain and slowly increments time by time. Tremor is the major side effect of PD where the entire body begins shaking. Besides, a person’s muscle may end up rigid or stiff and it may happen any portion of his body. PD influences the central apprehensive system which is happening because of the hardship of dopaminergic neurons brought about in a neuro-degenerative incubation. It is grouped beneath advancement clutter as patients who have PD appear with tremor, unyielding nature, postural shifts, and lessen in unconstrained advancements. There is no particular diagnosis process for PD. PD varies from one person to another person and the situation and history. MRI, CT, ultrasound of the brain, PET scans are common imaging tests to figure out this disease but these tests are not particularly effective. In this research, several tests ran on two types of data group - control and PD affected people. The dataset is collected from the Parkinson’s Progression Markers Initiative (PPMI) repository. Then MRI slices are processed from selected data group into the CNN models. Three CNN models are sent into this thesis work to extract features from the data group. The CNN models are InceptionV3, VGG16 and VGG19. These models are used in this research to compare and get better accuracy. Among these models VGG19 worked best in the dataset because the accuracy for VGG19 is 91.5% where VGG16 gives 88.5% and inceptionV3 gives 89.5% on detecting PD. Asaduzzaman A.F.M. Nazmus Sakib Sanjida Ali Shusmita S. M. Ashraf Kabir B. Computer Science 2021-06-01T03:47:21Z 2021-06-01T03:47:21Z 2020 2020-04 Thesis ID: 17101531 ID: 16201005 ID: 16301154 ID: 16301034 http://hdl.handle.net/10361/14457 en_US 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. 59 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Parkinson’s Disease (PD) Neurological condition Dopaminergic neurons PPMI MRI CNN Extract features VGG3 VGG16 VGG19 |
| spellingShingle |
Parkinson’s Disease (PD) Neurological condition Dopaminergic neurons PPMI MRI CNN Extract features VGG3 VGG16 VGG19 Asaduzzaman Sakib, A.F.M. Nazmus Shusmita, Sanjida Ali Kabir, S. M. Ashraf Detection of Parkinson’s disease from Neuro-imagery using deep neural network with transfer learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. |
| author2 |
Parvez, Mohammad Zavid |
| author_facet |
Parvez, Mohammad Zavid Asaduzzaman Sakib, A.F.M. Nazmus Shusmita, Sanjida Ali Kabir, S. M. Ashraf |
| format |
Thesis |
| author |
Asaduzzaman Sakib, A.F.M. Nazmus Shusmita, Sanjida Ali Kabir, S. M. Ashraf |
| author_sort |
Asaduzzaman |
| title |
Detection of Parkinson’s disease from Neuro-imagery using deep neural network with transfer learning |
| title_short |
Detection of Parkinson’s disease from Neuro-imagery using deep neural network with transfer learning |
| title_full |
Detection of Parkinson’s disease from Neuro-imagery using deep neural network with transfer learning |
| title_fullStr |
Detection of Parkinson’s disease from Neuro-imagery using deep neural network with transfer learning |
| title_full_unstemmed |
Detection of Parkinson’s disease from Neuro-imagery using deep neural network with transfer learning |
| title_sort |
detection of parkinson’s disease from neuro-imagery using deep neural network with transfer learning |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14457 |
| work_keys_str_mv |
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