Handbook of decision support systems for neurological disorders - Hemanth, D. Jude 1981- (Academic Press, an imprint of Elsevier, 2021)
Front Cover -- HANDBOOK OF DECISION SUPPORT SYSTEMS FOR NEUROLOGICAL DISORDERS -- HANDBOOK OF DECISION SUPPORT SYSTEMS FOR NEUROLOGICAL DISORDERS -- Copyright -- Contents -- Contributors -- Preface -- 1 - A review of deep learning-based disease detection in Alzheimer's patients -- 1.1 Introduction -- 1.1.1 Alzheimer's disease -- 1.1.1.1 Stages of Alzheimer's -- 1.1.1.2 Cause of Alzheimer's -- 1.1.1.3 Signs and symptoms -- 1.1.1.4 Anatomical changes in the brain -- 1.1.2 Deep learning -- 1.2 Literature review -- 1.2.1 Neuroimaging techniques used in Alzheimer's detection -- 1.2.1.1 Magnetic resonance imaging -- 1.2.1.2 Computer tomography -- 1.2.1.3 Positron emission tomography -- 1.2.2 Data resources -- 1.2.2.1 Alzheimer's disease Neuroimaging Initiative -- 1.2.2.2 Open Access Series of Imaging Studies -- 1.2.2.3 Minimal Interval Resonance Imaging in Alzheimer's Disease -- 1.2.3 Tools available for processing neuroimaging data -- 1.2.3.1 3D Slicer -- 1.2.3.2 BrainVoyager -- 1.2.3.3 CONN -- 1.2.3.4 FreeSurfer -- 1.2.3.5 SPM -- 1.2.4 Issues and challenges with medical imaging -- 1.2.5 Deep learning for imagery data -- 1.2.5.1 Layers of the CNN -- Convolution layer -- Nonlinearity layer -- Pooling layer -- Regularization -- 1.3 Methods of Alzheimer's detection using neuroimaging data -- 1.3.1 Feature-based methods -- 1.3.1.1 Voxel-level features -- 1.3.1.2 Vertex-level features -- 1.3.1.3 Hippocampus based -- 1.3.1.4 Miscellaneous -- 1.3.2 Deep learning-based methods -- 1.3.2.1 2D CNN-based methods -- 1.3.2.2 3D CNN-based methods -- 1.4 Comparison of detection methods -- 1.5 Conclusion -- References -- 2 - Brain tissue segmentation to detect schizophrenia in gray matter using MR images -- 2.1 Introduction -- 2.2 Data collection -- 2.3 Methods -- 2.3.1 Image enhancement and skull stripping -- 2.3.1.1 Image enhancement -- 2.3.1.2 Skull stripping
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