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Details
Table of Contents
Intro
Preface
Contents
Overview of Neurodegenerative Disorders
Overview of Neurodegenerative Disorders
1 Introduction
2 Neurodegenerative Disorders (NDDs)
2.1 Alzheimer's Disease
2.2 Parkinson's Disease
2.3 Huntington Disorder
2.4 Lewy Body Disease
2.5 Cerebral Aneurysm
2.6 Epilepsy
2.7 Spinocerebellar Ataxia (SCA)
2.8 Amyotrophic Lateral Sclerosis (ALS)
3 Conclusion
References
AI and Machine Learning Models for Neurodegenerative Disorders
Artificial Intelligence and Machine Learning Models for Diagnosing Neurodegenerative Disorders
1 Introduction
2 Description of Medical Examination
2.1 Brain Imaging
2.2 Clinical Tests
2.3 Biomarkers
2.4 Staging
3 Datasets for Diagnosing Neurodegenerative Disorders
3.1 Alzheimer Dataset
3.2 Parkinson Dataset
3.3 Huntington Dataset
3.4 Amyotrophic Lateral Sclerosis Dataset
4 Methodology of AI and ML Models for Diagnosing Neurodegenerative Disorder
5 AI and ML Models in Diagnosing Neurodegenerative Disorders
5.1 Convolutional Neural Network Model
5.2 Deep Learning Model
5.3 Long Short Term Memory Models
5.4 Graph Convolutional Network Model
5.5 Support Vector Machine Model
5.6 Random Forest Model
5.7 Survival Analysis Model
6 Contributions of AI and ML Models in Diagnosing Neurodegenerative Disorders
6.1 Contributions of DL Models
6.2 Contributions of CNN Models
6.3 Contributions of LSTM Models
6.4 Contributions of GCN Models
6.5 Contributions of SVM Models
6.6 Contributions of RF Models
6.7 Contributions of Hybrid Models
6.8 Contributions of Survival Analysis Models
7 Challenges and Opportunities for Diagnosing Neurodegenerative Disorders
8 Results and Discussion
9 Conclusion
References
Neurodegenerative Alzheimer's Disease Disorders and Deep Learning Approaches
1 Introduction
2 Proposed Work
3 Results
4 Discussions and Limitations
5 Conclusion
References
Yoga Practitioners and Non-yoga Practitioners to Deal Neurodegenerative Disease in Neuro Regions
1 Introduction
2 Grey Matter Volume (GM)
2.1 White Matter Volume (WM)
2.2 Cerebral Fluid (CF)
2.3 The Free Surfer Method
3 Yoga
4 Magnetic Resonance Imaging
5 Brain Age
6 Mechanism for Cortex Measurement
6.1 Normalization of MRI Data
6.2 Noise in MRI Data
6.3 Feature Selection
7 Recent Study
8 Conclusion
References
Machine Learning Models for Alzheimer's Disorders
Automated Electroencephalogram Temporal Lobe Signal Processing for Diagnosis of Alzheimer Disease
1 Introduction
2 Related Work
3 Methodology
4 Dataset Used in Experimentation
4.1 Details of Dataset 1
4.2 Details of Dataset 2
5 Deep Learning Model
6 Results and Discussion
7 Conclusion
References
Machine Learning Models for Alzheimer's Disease Detection Using OASIS Data
1 Introduction
2 Related Work
3 Understanding of Data
3.1 Data
Preface
Contents
Overview of Neurodegenerative Disorders
Overview of Neurodegenerative Disorders
1 Introduction
2 Neurodegenerative Disorders (NDDs)
2.1 Alzheimer's Disease
2.2 Parkinson's Disease
2.3 Huntington Disorder
2.4 Lewy Body Disease
2.5 Cerebral Aneurysm
2.6 Epilepsy
2.7 Spinocerebellar Ataxia (SCA)
2.8 Amyotrophic Lateral Sclerosis (ALS)
3 Conclusion
References
AI and Machine Learning Models for Neurodegenerative Disorders
Artificial Intelligence and Machine Learning Models for Diagnosing Neurodegenerative Disorders
1 Introduction
2 Description of Medical Examination
2.1 Brain Imaging
2.2 Clinical Tests
2.3 Biomarkers
2.4 Staging
3 Datasets for Diagnosing Neurodegenerative Disorders
3.1 Alzheimer Dataset
3.2 Parkinson Dataset
3.3 Huntington Dataset
3.4 Amyotrophic Lateral Sclerosis Dataset
4 Methodology of AI and ML Models for Diagnosing Neurodegenerative Disorder
5 AI and ML Models in Diagnosing Neurodegenerative Disorders
5.1 Convolutional Neural Network Model
5.2 Deep Learning Model
5.3 Long Short Term Memory Models
5.4 Graph Convolutional Network Model
5.5 Support Vector Machine Model
5.6 Random Forest Model
5.7 Survival Analysis Model
6 Contributions of AI and ML Models in Diagnosing Neurodegenerative Disorders
6.1 Contributions of DL Models
6.2 Contributions of CNN Models
6.3 Contributions of LSTM Models
6.4 Contributions of GCN Models
6.5 Contributions of SVM Models
6.6 Contributions of RF Models
6.7 Contributions of Hybrid Models
6.8 Contributions of Survival Analysis Models
7 Challenges and Opportunities for Diagnosing Neurodegenerative Disorders
8 Results and Discussion
9 Conclusion
References
Neurodegenerative Alzheimer's Disease Disorders and Deep Learning Approaches
1 Introduction
2 Proposed Work
3 Results
4 Discussions and Limitations
5 Conclusion
References
Yoga Practitioners and Non-yoga Practitioners to Deal Neurodegenerative Disease in Neuro Regions
1 Introduction
2 Grey Matter Volume (GM)
2.1 White Matter Volume (WM)
2.2 Cerebral Fluid (CF)
2.3 The Free Surfer Method
3 Yoga
4 Magnetic Resonance Imaging
5 Brain Age
6 Mechanism for Cortex Measurement
6.1 Normalization of MRI Data
6.2 Noise in MRI Data
6.3 Feature Selection
7 Recent Study
8 Conclusion
References
Machine Learning Models for Alzheimer's Disorders
Automated Electroencephalogram Temporal Lobe Signal Processing for Diagnosis of Alzheimer Disease
1 Introduction
2 Related Work
3 Methodology
4 Dataset Used in Experimentation
4.1 Details of Dataset 1
4.2 Details of Dataset 2
5 Deep Learning Model
6 Results and Discussion
7 Conclusion
References
Machine Learning Models for Alzheimer's Disease Detection Using OASIS Data
1 Introduction
2 Related Work
3 Understanding of Data
3.1 Data