Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives explores how deep learning revolutionizes neuroinformatics, covering the latest methods and applications of deep learning in analyzing neuroimaging data from EEG, MRI, PET, and more. The book addresses critical neurological disorders like Alzheimer’s disease, Mild Cognitive Impairment, Stroke, and Autism Spectrum Disorder, bridging the gap between neuroscience and artificial intelligence. It is an ideal resource for researchers, practitioners, and students with insights from leading experts.
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. iv 6. Dedication - p. v 7. List of contributors - p. xv 8. About the editor - p. xix 9. Preface - p. xxi 10. Chapter 1. Fundamentals of deep learning in neuroinformatics - p. 1 11. Chapter 2. Data preprocessing and augmentation techniques for neuroinformatics - p. 20 12. Chapter 3. Deep learning in stroke care - p. 49 13. Chapter 4. Deep learning for Alzheimer’s disease and mild cognitive impairment detection - p. 81 14. Chapter 5. Ensemble deep learning with generative augmentation for Alzheimer’s stage classification - p. 105 15. Chapter 6. Deep learning applications in Parkinson’s disease and movement disorders - p. 131 16. Chapter 7. Advancing early diagnosis of Parkinson’s disease: the role of deep learning and innovative diagnostic tools - p. 151 17. Chapter 8. Deep learning networks for multiple sclerosis lesion classification and segmentation—a review - p. 179 18. Chapter 9. Deep learning for autism spectrum disorder - p. 195 19. Chapter 10. Deep learning for neurodevelopmental and psychiatric disorders - p. 213 20. Chapter 11. Deep learning techniques for brain tumor detection and classification: trends, challenges, and future directions - p. 227 21. Chapter 12. Transfer learning and domain adaptation in neuroinformatics - p. 287 22. Chapter 13. Explainable artificial intelligence in neuroinformatics: enhancing transparency and understanding in computational neuroscience - p. 311 23. Index - p. 335
Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives explores how deep learning revolutionizes neuroinformatics, covering the latest methods and applications of deep learning in analyzing neuroimaging data from EEG, MRI, PET, and more. The book addresses critical neurological disorders like Alzheimer’s disease, Mild Cognitive Impairment, Stroke, and Autism Spectrum Disorder, bridging the gap between neuroscience and artificial intelligence. It is an ideal resource for researchers, practitioners, and students with insights from leading experts.
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. iv 6. Dedication - p. v 7. List of contributors - p. xv 8. About the editor - p. xix 9. Preface - p. xxi 10. Chapter 1. Fundamentals of deep learning in neuroinformatics - p. 1 11. Chapter 2. Data preprocessing and augmentation techniques for neuroinformatics - p. 20 12. Chapter 3. Deep learning in stroke care - p. 49 13. Chapter 4. Deep learning for Alzheimer’s disease and mild cognitive impairment detection - p. 81 14. Chapter 5. Ensemble deep learning with generative augmentation for Alzheimer’s stage classification - p. 105 15. Chapter 6. Deep learning applications in Parkinson’s disease and movement disorders - p. 131 16. Chapter 7. Advancing early diagnosis of Parkinson’s disease: the role of deep learning and innovative diagnostic tools - p. 151 17. Chapter 8. Deep learning networks for multiple sclerosis lesion classification and segmentation—a review - p. 179 18. Chapter 9. Deep learning for autism spectrum disorder - p. 195 19. Chapter 10. Deep learning for neurodevelopmental and psychiatric disorders - p. 213 20. Chapter 11. Deep learning techniques for brain tumor detection and classification: trends, challenges, and future directions - p. 227 21. Chapter 12. Transfer learning and domain adaptation in neuroinformatics - p. 287 22. Chapter 13. Explainable artificial intelligence in neuroinformatics: enhancing transparency and understanding in computational neuroscience - p. 311 23. Index - p. 335
