Diagnosis of Neurological Disorders Based on Deep Learning Techniques

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Editor
List of Contributors
Chapter 1 Introduction to Deep Learning Techniques for Diagnosis of Neurological Disorders
	Introduction
Chapter 2 A Comprehensive Study of Data Pre-Processing Techniques for Neurological Disease (NLD) Detection
	2.1 Introduction
	2.2 Related Works
	2.3 Methods
		2.3.1 Open-Access Data for NLD Disease Detection
			2.3.1.1 ADNI
			2.3.1.2 OASIS
			2.3.1.3 COBRE
			2.3.1.4 FBIRN
			2.3.1.5 PPMI
		2.3.2 ML Models for NLD Disease Detection
		2.3.3 Deep Learning Models for NLD Disease Detection
	2.4 Experimental Discussion of Data Pre-ProcessingTechniques for Brain Analysis
		2.4.1 Filtering
			2.4.1.1 Gaussian Filter
			2.4.1.2 Output
			2.4.1.3 Median Filter
			2.4.1.4 Output
			2.4.1.5 Weiner Filter
			2.4.1.6 Output
			2.4.1.7 Frequency Filter
			2.4.1.8 Output
			2.4.1.9 Unsharp Filter
			2.4.1.10 Output
		2.4.2 Normalization
			2.4.2.1 Spatial Normalization
			2.4.2.2 Intensity Normalization
			2.4.2.3 Output
			2.4.2.4 Histogram Normalization
			2.4.2.5 Output
	2.5 Conclusion
	References
Chapter 3 Classification of the Level of Alzheimer’s Disease Using Anatomical Magnetic Resonance Images Based on a Novel Deep Learning Structure
	3.1 Organization of the Chapter
	3.2 Introduction
		3.2.1 Motivation
		3.2.2 Contribution
	3.3 Materials and Methods
		3.3.1 Dataset
		3.3.2 Proposed Method
			3.3.2.1 Images Processing
			3.3.2.2 CNN Design
		3.3.3 Evaluation Metrics
	3.4 Results
	3.5 Discussion
	3.6 Conclusion
	3.7 Future Scopes
	References
Chapter 4 Detection of Alzheimer’s Disease Stages Based on Deep Learning Architectures from MRI Images
	4.1 Introduction
	4.2 Objectives of the Study
	4.3 Literature Review
		4.3.1 Early Diagnosis of Alzheimer’s Disease Using DL-Based Approaches
		4.3.2 A Transfer Learning Approach for Early Diagnosis of AD on MRI Images
		4.3.3 Classification of AD MRI Images Using a Hybrid CNN Technique
		4.3.4 Ensembles of DL-Based Network Models for the Early Diagnosis of the AD
	4.4 Gap Analysis
	4.5 Research Methodology of the Study
	4.6 Findings and Discussions
	4.7 Conclusion and Recommendations
	References
Chapter 5 Analysis on Detection of Alzheimer’s using Deep Neural Network
	5.1 Introduction
	5.2 A Study on Assorted Deep Neural Networks
		5.2.1 CNN
		5.2.2 LSTM
		5.2.3 RNN
		5.2.4 Autoencoder
		5.2.5 Perceptron
		5.2.6 DBN
		5.2.7 RBM
	5.3 Methodology
		5.3.1 Types of CNN
		5.3.2 ResNet
			5.3.2.1 ResNet’s Features
		5.3.3 GoogLeNet
			5.3.3.1 Features of GoogLeNet
		5.3.4 VGGNet-16 and VGGNet-19
			5.3.4.1 Summary of VGGNet Model
		5.3.5 AlexNet
			5.3.5.1 Details of the model
		5.3.6 LeNet
			5.3.6.1 LeNet’s Features
	5.4 Results and Discussion
		5.4.1 Experimental Analysis and Discussion of Datasets
			5.4.1.1 ADNI
			5.4.1.2 OASIS
		5.4.2 Analysis of Types of DNN
			5.4.2.1 ADNI Dataset
			5.4.2.2 OASIS Dataset
		5.4.3 Performance Measures
	5.5 Conclusion
	References
Chapter 6 Detection and Classification of Alzheimer’s Disease: A Deep Learning Approach with Predictor Variables
	6.1 Introduction
	6.2 Related Work
	6.3 Motivation and Objective
	6.4 Deep Neural Network
	6.5 Proposed Method
	6.6 Experimental Setup
	6.7 Dataset
	6.8 Performance Metrics
	6.9 Results and Discussion
	6.10 Conclusion
	References
Chapter 7 Classification of Brain Tumor Using Optimized Deep Neural Network Models
	7.1 Introduction
	7.2 Related Work
	7.3 Dataset Used for Proposed Method
		7.3.1 IBSR Dataset
		7.3.2 MRI Scan Images
	7.4 Performance Evaluation Method
		7.4.1 Accuracy
		7.4.2 Precision
		7.4.3 Recall
		7.4.4 F-Measure
		7.4.5 FP Rate
		7.4.6 TP Rate
	7.5 Methodology of Proposed DNN Models
		7.5.1 VGG16
		7.5.2 ResNet50
		7.5.3 Inception-V3
		7.5.4 Develop a Model Approach
		7.5.5 Pre-trained Model Approach
	7.6 Results and Discussion
	7.7 Conclusion
	References
Chapter 8 Fully Automated Segmentation of Brain Stroke Lesions Using Mask Region-Based Convolutional Neural Network
	8.1 Introduction
	8.2 Materials and Methods
		8.2.1 Imaging Dataset and Data Acquisition
		8.2.2 Mask Region-Based Convolutional Neural Network (Mask R-CNN)
		8.2.3 Backbone Structure
	8.3 Results and Discussion
	8.4 Conclusion
	Acknowledgments
	References
Chapter 9 Efficient Classification of Schizophrenia EEG Signals Using Deep Learning Methods
	9.1 Introduction
	9.2 Data Recording
	9.3 Methodology
		9.3.1 Time Domain Features
		9.3.2 Creating Spectrogram Images Using Short-Time Fourier Transform (STFT)
		9.3.3 Architecture of LSTM
		9.3.4 Architecture of VGG-16
		9.3.5 Architecture of AlexNet
	9.4 Results and Discussion
		9.4.1 Results of Binary Classification Using LSTM Model
		9.4.2 Results of Binary Classification Using VGG-16 and AlexNet Models
		9.4.3 Five-Fold Cross-Validation
	9.5 Conclusion
	References
Chapter 10 Implementation of a Deep Neural Network-Based Framework for Actigraphy Analysis and Prediction of Schizophrenia
	10.1 Introduction
	10.2 Related Work
		10.2.1 Actigraphy
	10.3 Materials and Method
	10.4 Results and Discussion
		10.4.1 Case (i)
		10.4.2 Case (ii)
	10.5 Conclusion
	References
Chapter 11 Evaluating Psychomotor Skills in Autism Spectrum Disorder Through Deep Learning
	11.1 Introduction
	11.2 Search Methodology
	11.3 Clinical Studies of ASD
		11.3.1 Robot-Assisted Diagnosis
		11.3.2 Eye Tracking
		11.3.3 Facial Scanning
		11.3.4 Gait Analysis for Stereotypical Movements
		11.3.5 Facial Fused Gait Analysis
		11.3.6 Electroencephalography
		11.3.7 Functional near-Infrared Spectroscopy (f-NIRS)
		11.3.8 MR Imaging (Structural and Functional)
	11.4 Results and Discussions
	11.5 Conclusion
		11.5.1 Declarations
	References
Chapter 12 Dementia Detection with Deep Networks Using Multi-Modal Image Data
	12.1 Introduction
	12.2 Related Work
		12.2.1 Structural Imaging in Dementia
		12.2.2 Molecular Imaging in Dementia
		12.2.3 Multi-Modality Imaging Solutions
	12.3 Methodology
		12.3.1 Image Acquisition
		12.3.2 Pre-Processing of Brain Scans
		12.3.3 Implementation of Multi-Modal Imaging Networks
	12.4 Results and Discussion
	12.5 Conclusion
	Acknowledgments
	Notes
	References
Chapter 13 The Importance of the Internet of Things in Neurological Disorder: A Literature Review
	13.1 Introduction
	13.2 Fundamentals of Deep Learning in Healthcare Systems
	13.3 Developments in Healthcare Systems and IoT
	13.4 IoT in Neurological Diseases
	13.5 Conclusions and Future Scope
	References
Index