Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease

Keyphrases

• Alzheimer's Disease 100%
• Machine Learning Based 100%
• Hierarchical Classification 100%
• Dementia Diseases 100%
• Frontotemporal Dementia 100%
• Semantic Variant PPA 40%
• Cortical Thickness 30%
• Primary Progressive Aphasia 30%
• Non-fluent 30%
• Agrammatism 30%
• Classification Accuracy 20%
• Classification Model 20%
• Cortical Atrophy 20%
• Classification Methods 10%
• Brain Magnetic Resonance Imaging 10%
• Principal Coordinate Analysis (PCoA) 10%
• Classification Performance 10%
• Three-dimensional (3D) 10%
• Feature Vector 10%
• Cross-validation Method 10%
• Cognitively Normal 10%
• Noise Reduction 10%
• Temporal Region 10%
• Atrophy 10%
• Dementia Patients 10%
• Machine Learning Classification 10%
• Subject Classification 10%
• Laplace-Beltrami Operator 10%
• Clinical Setting 10%
• Frontotemporal 10%
• Magnetic Resonance Imaging Scan 10%
• Hierarchical Manner 10%
• Linear Discriminant Analysis 10%
• Group Analysis 10%
• Classification Tree 10%
• Dementia Alzheimer 10%
• FreeSurfer 10%
• Anterior Temporal 10%
• Alzheimer's Dementia 10%
• Overlapping Patterns 10%
• Clinical Syndromes 10%
• Diagnostic Categories 10%
• Left Frontal Lobe 10%
• Specific Interventions 10%
• Clinical Subtypes 10%
• Clinician Diagnosis 10%
• Behavioral Variant FTD 10%
• Pair of Groups 10%
• Dementia Subtypes 10%
• Automated Classifier 10%
• Non-hierarchical 10%

Neuroscience

• Alzheimer's Disease 100%
• Frontotemporal Dementia 100%
• Primary Progressive Aphasia 37%
• Frontal Variant Frontotemporal Dementia 37%
• Magnetic Resonance Imaging 12%
• Frontal Lobe 12%