Handbook of Decision Support Systems for Neurological Disorders

by M.E. Hemanth B.E. (Editor)

Overview

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. - Includes applications of computer intelligence and decision support systems for the diagnosis and analysis of a variety of neurological disorders - Presents in-depth, technical coverage of computer-aided systems for tumor image classification, Alzheimer's disease detection, dementia detection using deep belief neural networks, and morphological approaches for stroke detection - Covers disease diagnosis for cerebral palsy using auto-encoder approaches, contrast enhancement for performance enhanced diagnosis systems, autism detection using fuzzy logic systems, and autism detection using generative adversarial networks - Written by engineers to help engineers, computer scientists, researchers and clinicians understand the technology and applications of decision support systems for neurological disorders

Product Details

ISBN-13:	9780128222720
Publisher:	Elsevier Science & Technology Books
Publication date:	03/30/2021
Sold by:	Barnes & Noble
Format:	eBook
Pages:	320
File size:	27 MB
Note:	This product may take a few minutes to download.

About the Author

Dr. D. Jude Hemanth is currently working as a professor in Department of ECE, Karunya University, Coimbatore, India. He also holds the position of “Visiting Professor” in Faculty of Electrical Engineering and Information Technology, University of Oradea, Romania. He also serves as the “Research Scientist” of Computational Intelligence and Information Systems (CI2S) Lab, Argentina; LAPISCO research lab, Brazil; RIADI Lab, Tunisia; Research Centre for Applied Intelligence, University of Craiova, Romania and e-health and telemedicine group, University of Valladolid, Spain.Dr. Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006 and Ph.D. from Karunya University in 2013. He has published 37 edited books with reputed publishers such as Elsevier, Springer and IET. His research areas include Computational Intelligence and Image processing. He has authored more than 200 research papers in reputed SCIE indexed International Journals and Scopus indexed International Conferences.

Table of Contents

1.A review of deep learning-based disease detection in Alzheimer's patients2. Brain tissue segmentation to detect schizophrenia in gray matter using MR images3. Detection of small tumors of the brain using medical imaging4. Fuzzy logic-based hybrid knowledge systems for the detection and diagnosis of childhood autism5. Artificial intelligence for risk prediction of Alzheimer's disease: a new promise for community health screening in theolder aged6. Cost-effective assistive device for motor neuron disease 7. EEG signal-based human emotion detection using an artificial neural network8. Multiview decision tree-based segmentation of tumors in MR brain medical images9. Multiclass SVM coupled with optimization techniques forsegmentation and classification of medical images10. Brain tissues segmentation in magnetic resonance imaging for the diagnosis of brain disorders using a convolutional neural network11. Fine motor skills and cognitive development using virtual reality-based games in children12. A CAD software application as a decision support system for ischemic stroke detection in the posterior fossa13. Optimization-based multilevel threshold image segmentation for identifying ischemic stroke lesion in brain MR images14. A study of machine learning algorithms used for detecting cognitive disorders associated with dyslexia15. A Critical Analysis and Review of Assistive Technology: Advancements, Laws, and Impact on Improving the Rehabilitation of Dysarthric Patients16. A comparative study on the application of machine learningalgorithms for neurodegenerative disease prediction

What People are Saying About This

From the Publisher

Examines the latest research in neuroadaptive technology and its applications to human-computer interaction, signal processing, machine learning and applied neuroscience