000 09370nam a22002657a 4500
008 201210s2024 a|||f bm|| 00| 0 eng d
024 7 _a0000-0002-8175-4554
_2ORCID
040 _aEG-CaNU
_cEG-CaNU
041 0 _aeng
_beng
_bara
082 _a610
100 0 _aAya Gamal Ali Ali
_93565
245 1 _aEARLY DIAGNOSIS OF ALZHEIMER’S DISEASE BY LEARNING FROM MULTI-MODAL DATA FUSION
_c/Aya Gamal Ali Ali
260 _c2024
300 _a111p.
_bill.
_c21 cm.
500 _3Supervisor: Dr. Mustafa Elattar
502 _aThesis (M.A.)—Nile University, Egypt, 2024 .
504 _a"Includes bibliographical references"
505 0 _aContents: Acknowledgments.................................................................................................I Abstract ...............................................................................................................II List of Figures......................................................................................................V List of Tables....................................................................................................VII List of Equations..............................................................................................VIII List of Abbreviation........................................................................................... IX 1. Introduction ......................................................................................................... 1 1.1Motivation ..................................................................................................... 1 1.2 Problem Statement ........................................................................................ 2 1.3 Thesis Outline and Summary of Contributions .............................................. 4 2. Background ......................................................................................................... 6 2.1Clinical Background ...................................................................................... 6 2.1.1 Alzheimer’s Disease ........................................................................... 6 2.1.2 How Alzheimer’s Disease Affects the Brain........................................ 7 2.1.3 Alzheimer’s Stage Determination and Its Progression....................... 12 2.2 Clinical Diagnosis Assessments ................................................................... 13 2.3 Challenges in Early Detection of Alzheimer’s Disease ................................. 15 2.4 Different Modalities for Diagnosis............................................................... 16 2.4.1 Magnetic Resonance Imaging (MRI)................................................ 16 2.4.2 Positron Emission Tomography (PET).............................................. 18 3. Related Work................................................................................................... 21 3.1 Datasets ...................................................................................................... 21 3.2 Pre-processing Pipeline................................................................................ 22 3.3 Input Management Approaches.................................................................... 27 3.4 Multimodal Data Fusion............................................................................... 28 3.4.1 Fusion Strategies............................................................................. 29 3.4.2 Analysis of Various Modalities in Relevant Studies...................... 30 3.4.3 Challenges with Multi-Modal Approach........................................ 31 3.5 Alzheimer’s disease Diagnosis using Traditional Methods............................ 32 iv 3.6 Alzheimer’s disease Diagnosis using Deep Learning Methods.................. 34 4. Automatic Early Diagnosis of Alzheimer's disease using Structural MRI Images ...................................................................................................... 40 4.1 Dataset........................................................................................................ 40 4.2 Data preprocessing .................................................................................... 42 4.3 Augmentation ............................................................................................ 43 4.4 3D classification models ............................................................................. 44 4.4.1 3D Convolution neural networks.................................................... 44 4.4.2 Modified 3D DenseNet201 based transfer learning ....................... 44 4.4.3 Vision Transformers........................................................................ 45 4.4.4 Channel and Spatial Attention-based DenseNet ........................... 47 4.5 Evaluation Metrics .................................................................................... 47 4.6 Experiments details and Results............................................................... 49 4.6.1 Dataset and preprocessing setup.................................................... 49 4.6.2 Ablation study ................................................................................ 50 4.6.3 Experimental setting....................................................................... 53 4.7 Discussion................................................................................................... 57 5. Enhancing Early Alzheimer’s Disease Diagnosis through Multimodal Data Fusion ...................................................................................................... 62 5.1 A Novel Diagnostic Model for Early Detection of Alzheimer’s Disease Based on Clinical and Neuroimaging Features ......................................... 64 5.1.1 Methodology .................................................................................... 64 5.2 Deep Feature Fusion Framework for Alzheimer’s Disease Staging using Neuroimaging Modalities................................................................... 75 5.2.1 Methodology .................................................................................... 75 5.2.2 Experiments Setup and Results..................................................... 79 5.2.3 Discussion ........................................................................................ 83 6. Conclusions and Future work .......................................................................... 86 6.1 Conclusion.................................................................................................. 86 6.2 Future directions........................................................................................ 87 Bibliography...........................................................................................................111
520 3 _aAbstract: Alzheimer’s Disease (AD) is a severe neurodegenerative disease. Early identification of AD is crucial for enhancing the quality of life for both patients and their families. Unfortunately, most existing diagnostic techniques rely on subjective assessments of behavioral and cognitive symptoms, leading to misdiagnosis. In recent years, advances in medical imaging technology have led to the emergence of neuroimaging-based methods for the diagnosis of early AD stages. However, these methods often rely on the analysis of a single modality, which may not capture the full complexity of the disease. Multimodal data fusion has been proposed as a promising approach to address this limitation by combining information from different modalities. Our study is divided into two phases, each with a distinct focus and methodology. In the initial phase, our focus is on utilizing structural MRI images as a sole modality for detecting AD as it is considered one of the most used modalities in AD diagnosis. A novel methodology is introduced to achieve this objective by discussing each component necessary to structure the entire system. Our proposed methodology surpasses previous studies in distinguishing between individuals with AD and cognitive normal (CN), AD and mild cognitive impairment (MCI), and MCI and CN, achieving AUC scores of 95.09, 91.28, and 88.42, respectively. Moving to the second phase, our work is extended to integrate different modalities. Specifically, we start by incorporating extracted neuroimaging and clinical features. This method of combining clinical and neuroimaging features extracted from MRI and PET images yielded superior results, achieving an AUC score of 97% for classifying between stable and progressive MCI. Following this, we develop an automated multimodal system to integrate MRI and PET images at an intermediate level of fusion, enabling the automatic diagnosis of AD. We achieve an AUC score of 97.67% with an accuracy (ACC) of 95.24% for the AD vs CN task. Keywords Alzheimer’s Disease (AD), Multimodal Data Fusion, Neuroimaging Features, 3D Image Classification, Structural MRI, Positron Emission Tomography, and Automated Diagnosis.
546 _aText in English, abstracts in English and Arabic
650 4 _ainformatics
655 7 _2NULIB
_aDissertation, Academic
_9187
690 _ainformatics
942 _2ddc
_cTH
999 _c10893
_d10893