Early prediction of Alzheimer's disease using convolutional neural network
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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Brac University
2020
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| Առցանց հասանելիություն: | http://hdl.handle.net/10361/13782 |
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10361-137822022-01-26T10:21:52Z Early prediction of Alzheimer's disease using convolutional neural network Abed, Mahjabeen Tamanna Nabil, Shanewas Ahmed Fatema, Umme Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Alzheimer's Disease(AD) VGG19 Residual Network(ResNet) Convolutinal Neural Network(CNN) Transfer learning Mild Cognitive Impairment(MCI) Magnetic Resonance Imaging(MRI) Computer networks This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 33-38). Neuroimaging can be a prospective instrument for the diagnosis of Mild Cognitive Impairment (MCI) along with its more severe stage, Alzheimer's disease (AD). High- dimensional classi cation methods have been commonly used to explore Magnetic Resonance Imaging (MRI) for automatic classi cation of neurodegenerative diseases like AD and MCI. Early AD or MCI can be diagnosed through proper examination of several brain biomarkers such as Cerebrospinal Fluid (CSF), Media Temporal Lobe atrophy (MTL) and so on. Abnormal concentrations of the mentioned biomarkers on MRI images can be a potential sign of AD or MCI. In the recent times, several high- dimensional classi cation techniques have been suggested to discriminate between AD and MCI on the basis of T1-weighted MRI of patients. These techniques have been implemented mostly from scratch, making it really di cult to achieve any meaningful result within a short span of time. Therefore, classi cation of AD is usually a very daunting and time consuming task. In our study, we trained high dimensional Deep Neural Network (DNN) models with transfer learning in order to achieve meaningful results very quickly. We have used three di erent DNN models for our study: VGG19, Inception v3 and ResNet50 to classify between AD, MCI and Cognitively Normal (CN) patients. Firstly, we implemented some pre-processing steps on the images and divided them into training, testing and validation sets. Secondly, we initialized these DNN models with the weights from pre-existing models trained on the imagenet dataset. Finally, we trained and evaluated all the DNN models. After relatively short amount of trainings (15 epochs), we achieved an approximate of 90% accuracy with VGG19, 85% accuracy with Inception v3 and 70% with ResNet50. Thus, we achieved excellent classi cation accuracy in a very short time with our research. Mahjabeen Tamanna Abed Shanewas Ahmed Nabil Umme Fatema B. Computer Science 2020-02-19T05:54:10Z 2020-02-19T05:54:10Z 2019 2019-08 Thesis ID 17101268 ID 12201087 ID 16101330 http://hdl.handle.net/10361/13782 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 |
Alzheimer's Disease(AD) VGG19 Residual Network(ResNet) Convolutinal Neural Network(CNN) Transfer learning Mild Cognitive Impairment(MCI) Magnetic Resonance Imaging(MRI) Computer networks |
| spellingShingle |
Alzheimer's Disease(AD) VGG19 Residual Network(ResNet) Convolutinal Neural Network(CNN) Transfer learning Mild Cognitive Impairment(MCI) Magnetic Resonance Imaging(MRI) Computer networks Abed, Mahjabeen Tamanna Nabil, Shanewas Ahmed Fatema, Umme Early prediction of Alzheimer's disease using convolutional neural network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Abed, Mahjabeen Tamanna Nabil, Shanewas Ahmed Fatema, Umme |
| format |
Thesis |
| author |
Abed, Mahjabeen Tamanna Nabil, Shanewas Ahmed Fatema, Umme |
| author_sort |
Abed, Mahjabeen Tamanna |
| title |
Early prediction of Alzheimer's disease using convolutional neural network |
| title_short |
Early prediction of Alzheimer's disease using convolutional neural network |
| title_full |
Early prediction of Alzheimer's disease using convolutional neural network |
| title_fullStr |
Early prediction of Alzheimer's disease using convolutional neural network |
| title_full_unstemmed |
Early prediction of Alzheimer's disease using convolutional neural network |
| title_sort |
early prediction of alzheimer's disease using convolutional neural network |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/13782 |
| work_keys_str_mv |
AT abedmahjabeentamanna earlypredictionofalzheimersdiseaseusingconvolutionalneuralnetwork AT nabilshanewasahmed earlypredictionofalzheimersdiseaseusingconvolutionalneuralnetwork AT fatemaumme earlypredictionofalzheimersdiseaseusingconvolutionalneuralnetwork |
| _version_ |
1814309564435136512 |