Deep Learning Assessment of Neurological Imaging provides an introduction to deep learning structures and pre-processing methods for detecting MRI anomalies. The book also provides a comprehensive accounting of deep learning research on MRI images for Alzheimer's disease, Parkinson's disease, and schizophrenia, and includes a discussion on current research issues and future objectives. This book is a valuable resource to guide new entrants in the field, helping them make a meaningful impact in their development efforts. The book concludes with a brief overview of problems and potential future advancements in the field.
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. Contributors - p. xi 8. About the editors - p. xv 9. Preface - p. xxi 10. Acknowledgment - p. xxiii 11. Chapter 1: Graph convolutional networks for diagnosis of Alzheimer's disease: a review - p. 1 12. Chapter 2: Deep learning in neurological disease and brain health: foundations, applications, and challenges - p. 25 13. Chapter 3: Deep learning in brain tumors - p. 53 14. Chapter 4: Deep learning in Parkinson's disease - p. 69 15. Chapter 5: Predicting biological age of the brain using magnetic resonance imaging and deep learning methods - p. 81 16. Chapter 6: Artificial intelligence for schizophrenia detection: a comparative review of machine learning and deep learning models - p. 99 17. Chapter 7: Alzheimer's disease diagnosis using optimized ensemble deep learning model - p. 119 18. Chapter 8: A framework for characterizing brain iron concentration in neurodegenerative disease using quantitative susceptibility mapping - p. 141 19. Chapter 9: Deep learning for ischemic stroke lesion segmentation: towards a unified approach - p. 159 20. Chapter 10: Advanced entropy feature engineering using EMD and VMD for schizophrenia classification - p. 175 21. Chapter 11: Issues and challenges of neurological disorders diagnosis research - p. 189 22. Chapter 12: Opportunities and future directions of neurological disorders diagnosis research - p. 201 23. Chapter 13: Neurology beyond deep learning - p. 215 24. Subject index - p. 225
Deep Learning Assessment of Neurological Imaging provides an introduction to deep learning structures and pre-processing methods for detecting MRI anomalies. The book also provides a comprehensive accounting of deep learning research on MRI images for Alzheimer's disease, Parkinson's disease, and schizophrenia, and includes a discussion on current research issues and future objectives. This book is a valuable resource to guide new entrants in the field, helping them make a meaningful impact in their development efforts. The book concludes with a brief overview of problems and potential future advancements in the field.
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. Contributors - p. xi 8. About the editors - p. xv 9. Preface - p. xxi 10. Acknowledgment - p. xxiii 11. Chapter 1: Graph convolutional networks for diagnosis of Alzheimer's disease: a review - p. 1 12. Chapter 2: Deep learning in neurological disease and brain health: foundations, applications, and challenges - p. 25 13. Chapter 3: Deep learning in brain tumors - p. 53 14. Chapter 4: Deep learning in Parkinson's disease - p. 69 15. Chapter 5: Predicting biological age of the brain using magnetic resonance imaging and deep learning methods - p. 81 16. Chapter 6: Artificial intelligence for schizophrenia detection: a comparative review of machine learning and deep learning models - p. 99 17. Chapter 7: Alzheimer's disease diagnosis using optimized ensemble deep learning model - p. 119 18. Chapter 8: A framework for characterizing brain iron concentration in neurodegenerative disease using quantitative susceptibility mapping - p. 141 19. Chapter 9: Deep learning for ischemic stroke lesion segmentation: towards a unified approach - p. 159 20. Chapter 10: Advanced entropy feature engineering using EMD and VMD for schizophrenia classification - p. 175 21. Chapter 11: Issues and challenges of neurological disorders diagnosis research - p. 189 22. Chapter 12: Opportunities and future directions of neurological disorders diagnosis research - p. 201 23. Chapter 13: Neurology beyond deep learning - p. 215 24. Subject index - p. 225
