MARC details
| 000 -LEADER |
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| fixed length control field |
09370nam a22002657a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
201210s2024 a|||f bm|| 00| 0 eng d |
| 024 7# - Author Identifier |
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| Standard number or code |
0000-0002-8175-4554 |
| Source of number or code |
ORCID |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
EG-CaNU |
| Transcribing agency |
EG-CaNU |
| 041 0# - Language Code |
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| Language code of text |
eng |
| Language code of abstract |
eng |
| -- |
ara |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
610 |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Aya Gamal Ali Ali |
| 245 1# - TITLE STATEMENT |
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| Title |
EARLY DIAGNOSIS OF ALZHEIMER’S DISEASE BY LEARNING FROM MULTI-MODAL DATA FUSION |
| Statement of responsibility, etc. |
/Aya Gamal Ali Ali |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Date of publication, distribution, etc. |
2024 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
111p. |
| Other physical details |
ill. |
| Dimensions |
21 cm. |
| 500 ## - GENERAL NOTE |
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| Materials specified |
Supervisor: Dr. Mustafa Elattar |
| 502 ## - Dissertation Note |
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| Dissertation type |
Thesis (M.A.)—Nile University, Egypt, 2024 . |
| 504 ## - Bibliography |
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| Bibliography |
"Includes bibliographical references" |
| 505 0# - Contents |
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| Formatted contents note |
Contents:<br/>Acknowledgments.................................................................................................I<br/> Abstract ...............................................................................................................II<br/> List of Figures......................................................................................................V<br/> List of Tables....................................................................................................VII<br/> List of Equations..............................................................................................VIII<br/> List of Abbreviation........................................................................................... IX<br/>1. Introduction ......................................................................................................... 1<br/>1.1Motivation ..................................................................................................... 1<br/>1.2 Problem Statement ........................................................................................ 2<br/>1.3 Thesis Outline and Summary of Contributions .............................................. 4<br/>2. Background ......................................................................................................... 6<br/>2.1Clinical Background ...................................................................................... 6<br/>2.1.1 Alzheimer’s Disease ........................................................................... 6<br/>2.1.2 How Alzheimer’s Disease Affects the Brain........................................ 7<br/>2.1.3 Alzheimer’s Stage Determination and Its Progression....................... 12<br/>2.2 Clinical Diagnosis Assessments ................................................................... 13<br/>2.3 Challenges in Early Detection of Alzheimer’s Disease ................................. 15<br/>2.4 Different Modalities for Diagnosis............................................................... 16<br/>2.4.1 Magnetic Resonance Imaging (MRI)................................................ 16<br/>2.4.2 Positron Emission Tomography (PET).............................................. 18<br/>3. Related Work................................................................................................... 21<br/>3.1 Datasets ...................................................................................................... 21<br/>3.2 Pre-processing Pipeline................................................................................ 22<br/>3.3 Input Management Approaches.................................................................... 27<br/>3.4 Multimodal Data Fusion............................................................................... 28<br/>3.4.1 Fusion Strategies............................................................................. 29<br/>3.4.2 Analysis of Various Modalities in Relevant Studies...................... 30<br/>3.4.3 Challenges with Multi-Modal Approach........................................ 31<br/>3.5 Alzheimer’s disease Diagnosis using Traditional Methods............................ 32<br/>iv<br/>3.6 Alzheimer’s disease Diagnosis using Deep Learning Methods.................. 34<br/>4. Automatic Early Diagnosis of Alzheimer's disease using Structural<br/>MRI Images ...................................................................................................... 40<br/>4.1 Dataset........................................................................................................ 40<br/>4.2 Data preprocessing .................................................................................... 42<br/>4.3 Augmentation ............................................................................................ 43<br/>4.4 3D classification models ............................................................................. 44<br/>4.4.1 3D Convolution neural networks.................................................... 44<br/>4.4.2 Modified 3D DenseNet201 based transfer learning ....................... 44<br/>4.4.3 Vision Transformers........................................................................ 45<br/>4.4.4 Channel and Spatial Attention-based DenseNet ........................... 47<br/>4.5 Evaluation Metrics .................................................................................... 47<br/>4.6 Experiments details and Results............................................................... 49<br/>4.6.1 Dataset and preprocessing setup.................................................... 49<br/>4.6.2 Ablation study ................................................................................ 50<br/>4.6.3 Experimental setting....................................................................... 53<br/>4.7 Discussion................................................................................................... 57<br/>5. Enhancing Early Alzheimer’s Disease Diagnosis through Multimodal<br/>Data Fusion ...................................................................................................... 62<br/>5.1 A Novel Diagnostic Model for Early Detection of Alzheimer’s Disease<br/>Based on Clinical and Neuroimaging Features ......................................... 64<br/>5.1.1 Methodology .................................................................................... 64<br/>5.2 Deep Feature Fusion Framework for Alzheimer’s Disease Staging<br/> using Neuroimaging Modalities................................................................... 75<br/>5.2.1 Methodology .................................................................................... 75<br/>5.2.2 Experiments Setup and Results..................................................... 79<br/>5.2.3 Discussion ........................................................................................ 83<br/>6. Conclusions and Future work .......................................................................... 86<br/>6.1 Conclusion.................................................................................................. 86<br/>6.2 Future directions........................................................................................ 87<br/>Bibliography...........................................................................................................111 |
| 520 3# - Abstract |
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| Abstract |
Abstract:<br/>Alzheimer’s Disease (AD) is a severe neurodegenerative disease. Early <br/>identification of AD is crucial for enhancing the quality of life for both <br/>patients and their families. Unfortunately, most existing diagnostic <br/>techniques rely on subjective assessments of behavioral and cognitive <br/>symptoms, leading to misdiagnosis. In recent years, advances in medical <br/>imaging technology have led to the emergence of neuroimaging-based <br/>methods for the diagnosis of early AD stages. However, these methods <br/>often rely on the analysis of a single modality, which may not capture the <br/>full complexity of the disease. Multimodal data fusion has been proposed <br/>as a promising approach to address this limitation by combining <br/>information from different modalities. Our study is divided into two <br/>phases, each with a distinct focus and methodology. In the initial phase, <br/>our focus is on utilizing structural MRI images as a sole modality for <br/>detecting AD as it is considered one of the most used modalities in AD <br/>diagnosis. A novel methodology is introduced to achieve this objective by <br/>discussing each component necessary to structure the entire system. Our <br/>proposed methodology surpasses previous studies in distinguishing <br/>between individuals with AD and cognitive normal (CN), AD and mild <br/>cognitive impairment (MCI), and MCI and CN, achieving AUC scores of <br/>95.09, 91.28, and 88.42, respectively.<br/>Moving to the second phase, our work is extended to integrate different <br/>modalities. Specifically, we start by incorporating extracted neuroimaging <br/>and clinical features. This method of combining clinical and neuroimaging <br/>features extracted from MRI and PET images yielded superior results, <br/>achieving an AUC score of 97% for classifying between stable and <br/>progressive MCI. Following this, we develop an automated multimodal <br/>system to integrate MRI and PET images at an intermediate level of fusion, <br/>enabling the automatic diagnosis of AD. We achieve an AUC score of <br/>97.67% with an accuracy (ACC) of 95.24% for the AD vs CN task.<br/>Keywords<br/>Alzheimer’s Disease (AD), Multimodal Data Fusion, Neuroimaging Features, 3D<br/>Image Classification, Structural MRI, Positron Emission Tomography, and Automated <br/>Diagnosis. |
| 546 ## - Language Note |
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| Language Note |
Text in English, abstracts in English and Arabic |
| 650 #4 - Subject |
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| Subject |
informatics |
| 655 #7 - Index Term-Genre/Form |
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| Source of term |
NULIB |
| focus term |
Dissertation, Academic |
| 690 ## - Subject |
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| School |
informatics |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| 655 #7 - Index Term-Genre/Form |
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| -- |
187 |