+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)


Handbook of Decision Support Systems for Neurological Disorders

  • ID: 5203986
  • Book
  • April 2021
  • 332 Pages
  • Elsevier Science and Technology
Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer aided diagnosis systems for neurological disorders. Computer-aided decision support systems for different medical imaging modalities are available. This is the first book concentrating only on the decision support systems for neurological disorders. Due to the increase in the prevalence of diseases such as Alzheimer, Parkinson's, Dementia, this book will have significant importance in the medical field. Medical practitioners are finding it difficult to accurately assess/diagnose the neural problems of human beings, and need the support of engineering approaches for solving this problem. The topics of this book include two parts: (a) various recent computational approaches and (b) different types of neurological disorders. The computational approaches cover topics such as deep convolution neural networks, Generative Adversarial Networks, Auto encoders, Recurrent neural networks, and modified/hybrid artificial neural networks. The neurological disorders include Alzheimers, Parkinson's Disease, dementia, brain tumors, cerebral palsy, degenerative neural disorders, and more.
  • Includes applications of Computer Intelligence and Decision Support Systems to the diagnosis and analysis of a variety of neurological disorders
  • Includes 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
Note: Product cover images may vary from those shown
1. Deep learning-based disease detection in Alzheimer's patients 2. Brain tissue Segmentation to detect Schizophrenia in Gray Matter Using MR Images 3. Detection of small tumors of the brain using medical imaging 4. Fuzzy Logic-based Hybrid Knowledge Systems for Detection and Diagnosis of Childhood Autism 5. Artificial Intelligence for Risk Prediction of Alzheimer's Disease: A New Promise for Community Health Screening in the Older Aged 6. Cost Effective Assistive Device for Motor Neuron Disease 7. EEG signal based human emotion detection using artificial neural network 8. Multiview Decision Tree based Segmentation of Tumor in MR Brain Medical Images 9. Multiclass SVM Coupled with Optimization Techniques for Segmentation and Classification of Medical Images 10. Brain Tissues Segmentation in Magnetic Resonance Imaging for Diagnosis of Brain Disorders using Convolutional Neural Network 11. Fine Motor Skills and Cognitive Development Using Virtual Reality Based Games in Children 12. A CAD Software Application as Decision Support System for Ischemic Stroke Detection in Posterior Fossa 13. Optimization-based multilevel threshold image segmentation for identifying ischemic stroke lesion in brain MR images 14. A study of machine learning algorithms used for detecting cognitive disorders associated with dyslexia 15. A Critical Analysis and Review on Assistive Technology: Advancements, laws and Impact for Improving the Rehabilitation of Dysarthric Patients 16. A Comparative study on Application of Machine Learning Algorithms for Neurodegenerative Disease Prediction
Note: Product cover images may vary from those shown
Hemanth, Jude D.
Dr. D. Jude Hemanth received his PhD in Medical Image Analysis Using Soft Computing Techniques from Karunya University, India. He currently is an Associate Professor at Karunya University with research interests in Soft Computing, Biomedical Image Processing, and Optimization Techniques. He lectures on Biomedical Instrumentation, Neural Networks, Fuzzy Systems, Soft Computing, Digital Image Processing, and Multimedia Compression Techniques. He has been a prolific author and editor of many books and book chapters, including Nature Inspired Optimization Techniques for Image Processing Applications, Springer; Imaging and Sensing for Unmanned Aerial Vehicles, Institution of Engineering and Technology; Intelligent Data Communication Technologies and Internet of Things, Springer; Artificial Intelligence Techniques for Satellite Image Analysis, Springer, Emerging Trends in Computing and Expert Technology, Springer; Artificial Intelligence Techniques for Medical Image Analysis, VDM Verlag; Intelligent Data Analysis for Biomedical Applications, Academic Press; and Telemedicine Technologies, Academic Press, among others.
Note: Product cover images may vary from those shown