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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. ix 7. Preface - p. xi 8. Chapter 1. A review of deep learning-based disease detection in Alzheimer's patients - p. 1 9. Chapter 2. Brain tissue segmentation to detect schizophrenia in gray matter using MR images - p. 21 10. Chapter 3. Detection of small tumors of the brain using medical imaging - p. 33 11. Chapter 4. Fuzzy logic-based hybrid knowledge systems for the detection and diagnosis of childhood autism - p. 55 12. Chapter 5. Artificial intelligence for risk prediction of Alzheimer's disease: a new promise for community health screening in the older aged - p. 71 13. Chapter 6. Cost-effective assistive device for motor neuron disease - p. 89 14. Chapter 7. EEG signal-based human emotion detection using an artificial neural network - p. 107 15. Chapter 8. Multiview decision tree-based segmentation of tumors in MR brain medical images - p. 125 16. Chapter 9. Multiclass SVM coupled with optimization techniques for segmentation and classification of medical images - p. 143 17. Chapter 10. Brain tissues segmentation in magnetic resonance imaging for the diagnosis of brain disorders using a convolutional neural network - p. 167 18. Chapter 11. Fine motor skills and cognitive development using virtual reality-based games in children - p. 187 19. Chapter 12. A CAD software application as a decision support system for ischemic stroke detection in the posterior fossa - p. 203 20. Chapter 13. Optimization-based multilevel threshold image segmentation for identifying ischemic stroke lesion in brain MR images - p. 223 21. Chapter 14. A study of machine learning algorithms used for detecting cognitive disorders associated with dyslexia - p. 245 22. Chapter 15. A Critical Analysis and Review of Assistive Technology: Advancements, Laws, and Impact on Improving the Rehabilitation of Dysarthric Patients - p. 263 23. Chapter 16. A comparative study on the application of machine learning algorithms for neurodegenerative disease prediction - p. 283 24. Index - p. 303
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. ix 7. Preface - p. xi 8. Chapter 1. A review of deep learning-based disease detection in Alzheimer's patients - p. 1 9. Chapter 2. Brain tissue segmentation to detect schizophrenia in gray matter using MR images - p. 21 10. Chapter 3. Detection of small tumors of the brain using medical imaging - p. 33 11. Chapter 4. Fuzzy logic-based hybrid knowledge systems for the detection and diagnosis of childhood autism - p. 55 12. Chapter 5. Artificial intelligence for risk prediction of Alzheimer's disease: a new promise for community health screening in the older aged - p. 71 13. Chapter 6. Cost-effective assistive device for motor neuron disease - p. 89 14. Chapter 7. EEG signal-based human emotion detection using an artificial neural network - p. 107 15. Chapter 8. Multiview decision tree-based segmentation of tumors in MR brain medical images - p. 125 16. Chapter 9. Multiclass SVM coupled with optimization techniques for segmentation and classification of medical images - p. 143 17. Chapter 10. Brain tissues segmentation in magnetic resonance imaging for the diagnosis of brain disorders using a convolutional neural network - p. 167 18. Chapter 11. Fine motor skills and cognitive development using virtual reality-based games in children - p. 187 19. Chapter 12. A CAD software application as a decision support system for ischemic stroke detection in the posterior fossa - p. 203 20. Chapter 13. Optimization-based multilevel threshold image segmentation for identifying ischemic stroke lesion in brain MR images - p. 223 21. Chapter 14. A study of machine learning algorithms used for detecting cognitive disorders associated with dyslexia - p. 245 22. Chapter 15. A Critical Analysis and Review of Assistive Technology: Advancements, Laws, and Impact on Improving the Rehabilitation of Dysarthric Patients - p. 263 23. Chapter 16. A comparative study on the application of machine learning algorithms for neurodegenerative disease prediction - p. 283 24. Index - p. 303
