Handbook of Decision Support Systems for Neurological Disorders

1st Edition - March 30, 2021
Imprint: Academic Press
Editor: D. Jude Hemanth
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 2 7 1 - 3
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 2 7 2 - 0

Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided diagnosis systems for neurological disord… Read more

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Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided diagnosis systems for neurological disorders. While computer-aided decision support systems for different medical imaging modalities are available, this is the first book to solely concentrate on decision support systems for neurological disorders. Due to the increase in the prevalence of diseases such as Alzheimer, Parkinson’s and Dementia, this book will have significant importance in the medical field. Topics discussed include recent computational approaches, different types of neurological disorders, deep convolution neural networks, generative adversarial networks, auto encoders, recurrent neural networks, and modified/hybrid artificial neural networks.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Chapter 1. A review of deep learning-based disease detection in Alzheimer's patients
1.1. Introduction
1.2. Literature review
1.3. Methods of Alzheimer's detection using neuroimaging data
1.4. Comparison of detection methods
1.5. Conclusion
Chapter 2. Brain tissue segmentation to detect schizophrenia in gray matter using MR images
2.1. Introduction
2.2. Data collection
2.3. Methods
2.4. Results and discussion
2.5. Conclusion
Chapter 3. Detection of small tumors of the brain using medical imaging
3.1. Introduction
3.2. Types of medical images used in the application
3.3. Theoretical background of the application
3.4. Description of the application
3.5. Testing, installing, and using the application
3.6. Conclusions
Chapter 4. Fuzzy logic-based hybrid knowledge systems for the detection and diagnosis of childhood autism
4.1. Introduction
4.2. The advent of machine learning: a new horizon for autism
4.3. Fuzzy-based systems for autism
4.4. Moving ahead: virtual reality for autism
4.5. Current scenario and scope of improvement
4.6. Discussion and conclusion
List of abbreviations
Chapter 5. Artificial intelligence for risk prediction of Alzheimer's disease: a new promise for community health screening in the older aged
5.1. Introduction
5.2. Etiology and risk factors
5.3. Screening and early detection
5.4. Current methods of early detection
5.5. The rise of AI—a new promise for community health screening
5.6. Common algorithms used in ML for dementia and AD detection
5.7. Artificial intelligence methodologies for screening AD: concept examples
5.8. Promises and challenges of AI applications for predicting AD
5.9. Conclusions and future direction
Chapter 6. Cost-effective assistive device for motor neuron disease
6.1. Introduction
6.2. Motor neuron diseases
6.3. System configuration
6.4. Experimental results
6.5. Conclusion
Chapter 7. EEG signal-based human emotion detection using an artificial neural network
7.1. Introduction
7.2. EEG signal data acquisition
7.3. Statistical features extracted from EEG
7.4. Various ANN methods to classify EEG data
7.5. Classification of EEG-based emotion using ANN
7.6. Experimental analysis
7.7. Conclusion
Chapter 8. Multiview decision tree-based segmentation of tumors in MR brain medical images
8.1. Introduction
8.2. Multiview decision tree-based segmentation
8.3. Results and discussion
8.4. Conclusion
Chapter 9. Multiclass SVM coupled with optimization techniques for segmentation and classification of medical images
9.1. Introduction
9.2. Classification of support vector machine
9.3. Parameter tuning of multiclass SVM using optimization algorithms
9.4. Results and discussion
9.5. Conclusion
Chapter 10. Brain tissues segmentation in magnetic resonance imaging for the diagnosis of brain disorders using a convolutional neural network
10.1. Introduction
10.2. Materials and methods
10.3. Results and discussion
10.4. Conclusion and future work
Chapter 11. Fine motor skills and cognitive development using virtual reality-based games in children
11.1. Introduction
11.2. Neurological disorders in children and the role of VR games in children's rehabilitation
11.3. Leap Motion Controller and Unity3D
11.4. Game design and working
11.5. Experimental results and discussion
11.6. Future work
11.7. Conclusions
Chapter 12. A CAD software application as a decision support system for ischemic stroke detection in the posterior fossa
12.1. Introduction
12.2. Computer-aided diagnosis system implementation
12.3. Proposed CAD system for ischemic stroke detection
12.4. Experimental results and discussions
12.5. Conclusion
Chapter 13. Optimization-based multilevel threshold image segmentation for identifying ischemic stroke lesion in brain MR images
13.1. Introduction
13.2. Methodology
13.3. Results and discussion
13.4. Conclusion
Chapter 14. A study of machine learning algorithms used for detecting cognitive disorders associated with dyslexia
14.1. Introduction to neurological disorders
14.2. Classification of neurological disorders
14.3. Machine learning algorithms
14.4. Conclusion
Chapter 15. A Critical Analysis and Review of Assistive Technology: Advancements, Laws, and Impact on Improving the Rehabilitation of Dysarthric Patients
15.1. Introduction
15.2. Dysarthria
15.3. Assistive technologies for dysarthria
15.4. Design considerations in the development of Assistive Technology
15.5. Assistive technology laws for improving the rehabilitation of dysarthric patients
15.6. Impact of assistive technology on quality of life of dysarthric individuals
15.7. Inference
15.8. Summary of observations
15.9. Conclusion
Chapter 16. A comparative study on the application of machine learning algorithms for neurodegenerative disease prediction
16.1. Introduction
16.2. Literature survey
16.3. Description of the dataset
16.4. Support vector machine
16.5. Decision tree
16.6. Random forest tree
16.7. Results and discussions
16.8. Conclusion and future enhancements
Index

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Handbook of Decision Support Systems for Neurological Disorders

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D. Jude Hemanth

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