An interpretable deep learning approach to detect Alzheimer using MRI images
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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| Lingua: | English |
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Brac University
2023
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| Accesso online: | http://hdl.handle.net/10361/19868 |
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10361-19868 |
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10361-198682023-08-28T05:17:40Z An interpretable deep learning approach to detect Alzheimer using MRI images Oni, Farhan Anzum Hossain Sayem, Kazi Sazzad Rahman, Mushfiqur Kabir, Sanjida Bhuiyan, Fardeen Yousuf Alam, Dr. Md. Ashraful Department of Computer Science and Engineering, Brac University MRI Deep learning Convolutional Neural Networks (CNN) Multiclass classification approach VGG-19 ResNet50 V2 DenseNet-169 Inception V3 Augmentation Explainable AI (XAI) Gradient-Weighted Class Activation Mapping(GradCam) Cognitive learning theory (Deep learning) Alzheimer's disease. Neural networks (Computer science) Brain -- Imaging -- Mathematical models. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 35-37). Alzheimer’s disease (AD) is a serious neurological condition that causes loss of long term memory, cognitive difficulties, disorientation, inconsistent behavior, and even tually death. Also, AD is caused by the destruction of brain cells that are responsible for proper brain function. The main focus of our research is to provide an efficient model for the rapid diagnosis of Alzheimer’s disease. In this research, we design and demonstrate an interpretable deep-learning approach to detect Alzheimer’s us ing MRI images. For the experiment, brain MRIs are utilized, and by using this data, the model is able to determine the disease. Additionally, this model is de signed based on multiclass classification (MildDemented, ModerateDemented, Non Demented, VeryMildDemented) for helping patients belonging to different phases of Alzheimer’s disease. For this research, we experimented with four different ar chitectures of Convolutional Neural Networks. From the models, we obtained an accuracy of 92.65% for VGG-19, 89.18% for DenseNet-169, 87.84% for ResNet-50 V2, and 80.10% for Inception V3. By comparing and contrasting the performance of the models, the result can be improved by up to 92.65%, and it is decided to im plement the best-performing architecture (VGG19) into the system. Although there was a lack of data and it was difficult to tell the difference between a brain suffering from Alzheimer’s disease and a normal brain, the findings obtained revealed accu rate identification and categorization of Alzheimer’s disease and its phases. Lastly, GradCam (Gradient-Weighted Class Activation Mapping) is implemented to make the application of Explainable AI(XAI) apparent. Therefore, the proposed system would enable the detection and interpretation of Alzheimer’s disease effectively. Farhan Anzum Oni Kazi Sazzad Hossain Sayem Mushfiqur Rahman Sanjida Kabir Fardeen Yousuf Bhuiyan B. Computer Science and Engineering 2023-08-27T04:49:01Z 2023-08-27T04:49:01Z 2023 2023-01 Thesis ID: 19101048 ID: 19301155 ID: 19301149 ID: 18301225 ID: 18201041 http://hdl.handle.net/10361/19868 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. 37 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
MRI Deep learning Convolutional Neural Networks (CNN) Multiclass classification approach VGG-19 ResNet50 V2 DenseNet-169 Inception V3 Augmentation Explainable AI (XAI) Gradient-Weighted Class Activation Mapping(GradCam) Cognitive learning theory (Deep learning) Alzheimer's disease. Neural networks (Computer science) Brain -- Imaging -- Mathematical models. |
| spellingShingle |
MRI Deep learning Convolutional Neural Networks (CNN) Multiclass classification approach VGG-19 ResNet50 V2 DenseNet-169 Inception V3 Augmentation Explainable AI (XAI) Gradient-Weighted Class Activation Mapping(GradCam) Cognitive learning theory (Deep learning) Alzheimer's disease. Neural networks (Computer science) Brain -- Imaging -- Mathematical models. Oni, Farhan Anzum Hossain Sayem, Kazi Sazzad Rahman, Mushfiqur Kabir, Sanjida Bhuiyan, Fardeen Yousuf An interpretable deep learning approach to detect Alzheimer using MRI images |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Alam, Dr. Md. Ashraful |
| author_facet |
Alam, Dr. Md. Ashraful Oni, Farhan Anzum Hossain Sayem, Kazi Sazzad Rahman, Mushfiqur Kabir, Sanjida Bhuiyan, Fardeen Yousuf |
| format |
Thesis |
| author |
Oni, Farhan Anzum Hossain Sayem, Kazi Sazzad Rahman, Mushfiqur Kabir, Sanjida Bhuiyan, Fardeen Yousuf |
| author_sort |
Oni, Farhan Anzum |
| title |
An interpretable deep learning approach to detect Alzheimer using MRI images |
| title_short |
An interpretable deep learning approach to detect Alzheimer using MRI images |
| title_full |
An interpretable deep learning approach to detect Alzheimer using MRI images |
| title_fullStr |
An interpretable deep learning approach to detect Alzheimer using MRI images |
| title_full_unstemmed |
An interpretable deep learning approach to detect Alzheimer using MRI images |
| title_sort |
interpretable deep learning approach to detect alzheimer using mri images |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/19868 |
| work_keys_str_mv |
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