Alzheimer’s Disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| פורמט: | Thesis |
| שפה: | English |
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Brac University
2021
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| גישה מקוונת: | http://hdl.handle.net/10361/15161 |
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10361-15161 |
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10361-151612022-01-26T10:08:22Z Alzheimer’s Disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks Awwal, Alvina Shomee, Homaira Huda Sadat, Sayed Us Amin, Sadia Nur Parvez, Mohammad Zavid Reza, Md Tanzim Department of Computer Science and Engineering, Brac University Convolutional Neural Network (CNN) Alzheimer’s Disease (AD) Transfer Learning Neural Network (NN) MRI Deep Learning Average Ensemble VGG19 Inception-ResNet-v2 ResNet152v2 EfficientNetB5 EfficientNetB6 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 (page 30-33). Alzheimer’s disease (AD) is a neurological disease that affects the healthy cells of the brain and results in people having long-term memory loss, thinking problems, disorientation, behavioral inconsistencies and finally death. When the disease gets detected, the pathological load is already high and there is no way of coming back from there. This neurodegenerative disease consists of three general stages which we classified in our research that includes very mild (early stage), mild (middle stage) and finally, the moderate stage (late stage). We implemented 5 existing efficient and recent CNN models such as VGG19, Inception-ResNet-v2, ResNet152v2, EfficientNetB5 and EfficientNetB6 including one model of our own. Later, we did ensembling operations thrice with multiple combinations of the models to enhance our outcome and that was achieved since this gave improved accuracy of up to around 96% compared to the individual models where the maximum was 92.2% from EfficientNetB5. The results achieved showed precise detection and classification of AD and its stages even though data was limited and it was a challenge differentiating a healthy brain from that of a subject with AD. Alvina Awwal Homaira Huda Shomee Sayed Us Sadat Sadia Nur Amin B. Computer Science 2021-10-07T03:56:54Z 2021-10-07T03:56:54Z 2021 2021-01 Thesis ID 17101074 ID 17101061 ID 17101364 ID 17101397 http://hdl.handle.net/10361/15161 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. 33 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Convolutional Neural Network (CNN) Alzheimer’s Disease (AD) Transfer Learning Neural Network (NN) MRI Deep Learning Average Ensemble VGG19 Inception-ResNet-v2 ResNet152v2 EfficientNetB5 EfficientNetB6 |
| spellingShingle |
Convolutional Neural Network (CNN) Alzheimer’s Disease (AD) Transfer Learning Neural Network (NN) MRI Deep Learning Average Ensemble VGG19 Inception-ResNet-v2 ResNet152v2 EfficientNetB5 EfficientNetB6 Awwal, Alvina Shomee, Homaira Huda Sadat, Sayed Us Amin, Sadia Nur Alzheimer’s Disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks |
| 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 Awwal, Alvina Shomee, Homaira Huda Sadat, Sayed Us Amin, Sadia Nur |
| format |
Thesis |
| author |
Awwal, Alvina Shomee, Homaira Huda Sadat, Sayed Us Amin, Sadia Nur |
| author_sort |
Awwal, Alvina |
| title |
Alzheimer’s Disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks |
| title_short |
Alzheimer’s Disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks |
| title_full |
Alzheimer’s Disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks |
| title_fullStr |
Alzheimer’s Disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks |
| title_full_unstemmed |
Alzheimer’s Disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks |
| title_sort |
alzheimer’s disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15161 |
| work_keys_str_mv |
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| _version_ |
1814307486612586496 |