Multi-classification based Alzheimer's disease detection with comparative analysis from brain MRI scans using deep learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
| Main Authors: | , , , , |
|---|---|
| 其他作者: | |
| 格式: | Thesis |
| 语言: | English |
| 出版: |
Brac University
2022
|
| 主题: | |
| 在线阅读: | http://hdl.handle.net/10361/16552 |
| id |
10361-16552 |
|---|---|
| record_format |
dspace |
| spelling |
10361-165522022-04-19T21:01:30Z Multi-classification based Alzheimer's disease detection with comparative analysis from brain MRI scans using deep learning Kabir, Azmain Kabir, Farishta Mahmud, Md. Abu Hasib Sinthia, Sanzida Alam Azam, S. M. Rakibul Parvez, Mohammad Zavid Hussain, Emtiaz Department of Computer Science and Engineering, Brac University Alzheimer's disease CNN Multi-class Binary class MRI Deep learning Early detection Comparative analysis 18-layer 3D Scans OASIS-1 Artificial intelligence Machine learning. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 41-42). The neurodegenerative Alzheimer's Disease is the most widely recognized cause of `Dementia' and was allegedly the 7th highest cause of death globally. Nevertheless, there is still no conclusive test for distinguishing Alzheimer's disease. Our proposed model eliminates these challenges in an effective manner. The technique fits and analyzes different classes in a single setting and requires signi ficantly less previ- ous apprehension. Several handcrafted or prede ned machine learning and deep learning models have been implemented in this fi eld of study. Our proposed multi- classi cation model is primarily implemented based on the Open Access Series of Imaging Studies (OASIS) data and suggests an 18-layer architecture. We have im- plemented a unique preprocessing approach of all three anatomical planes of the MRI scans in a single sequential model, which was also evaluated afterward. The research also explores a comparative study among multiple and binary classes in terms of performance and effciency. Prede ned models such as InceptionV3 and VGG19 have also been brought to comparison to measure the model's reliability. Our multiclass setting shows an accuracy of over 80%, which is higher than most of the existing multi-classi fication models in this dataset. Moreover, the in-depth comparative study using binary classi cation shows a signi ficant accuracy of over 92%, which ensures the overall efficacy of the model. Azmain Kabir Farishta Kabir Md. Abu Hasib Mahmud Sanzida Alam Sinthia S. M. Rakibul Azam B. Computer Science 2022-04-19T05:34:13Z 2022-04-19T05:34:13Z 2021 2021-10 Thesis ID 18101576 ID 18101697 ID 18101189 ID 18101219 ID 18101123 http://hdl.handle.net/10361/16552 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. 42 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Alzheimer's disease CNN Multi-class Binary class MRI Deep learning Early detection Comparative analysis 18-layer 3D Scans OASIS-1 Artificial intelligence Machine learning. |
| spellingShingle |
Alzheimer's disease CNN Multi-class Binary class MRI Deep learning Early detection Comparative analysis 18-layer 3D Scans OASIS-1 Artificial intelligence Machine learning. Kabir, Azmain Kabir, Farishta Mahmud, Md. Abu Hasib Sinthia, Sanzida Alam Azam, S. M. Rakibul Multi-classification based Alzheimer's disease detection with comparative analysis from brain MRI scans using deep learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Parvez, Mohammad Zavid |
| author_facet |
Parvez, Mohammad Zavid Kabir, Azmain Kabir, Farishta Mahmud, Md. Abu Hasib Sinthia, Sanzida Alam Azam, S. M. Rakibul |
| format |
Thesis |
| author |
Kabir, Azmain Kabir, Farishta Mahmud, Md. Abu Hasib Sinthia, Sanzida Alam Azam, S. M. Rakibul |
| author_sort |
Kabir, Azmain |
| title |
Multi-classification based Alzheimer's disease detection with comparative analysis from brain MRI scans using deep learning |
| title_short |
Multi-classification based Alzheimer's disease detection with comparative analysis from brain MRI scans using deep learning |
| title_full |
Multi-classification based Alzheimer's disease detection with comparative analysis from brain MRI scans using deep learning |
| title_fullStr |
Multi-classification based Alzheimer's disease detection with comparative analysis from brain MRI scans using deep learning |
| title_full_unstemmed |
Multi-classification based Alzheimer's disease detection with comparative analysis from brain MRI scans using deep learning |
| title_sort |
multi-classification based alzheimer's disease detection with comparative analysis from brain mri scans using deep learning |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/16552 |
| work_keys_str_mv |
AT kabirazmain multiclassificationbasedalzheimersdiseasedetectionwithcomparativeanalysisfrombrainmriscansusingdeeplearning AT kabirfarishta multiclassificationbasedalzheimersdiseasedetectionwithcomparativeanalysisfrombrainmriscansusingdeeplearning AT mahmudmdabuhasib multiclassificationbasedalzheimersdiseasedetectionwithcomparativeanalysisfrombrainmriscansusingdeeplearning AT sinthiasanzidaalam multiclassificationbasedalzheimersdiseasedetectionwithcomparativeanalysisfrombrainmriscansusingdeeplearning AT azamsmrakibul multiclassificationbasedalzheimersdiseasedetectionwithcomparativeanalysisfrombrainmriscansusingdeeplearning |
| _version_ |
1814309207019618304 |