Deep Learning Assessment of Neurological Imaging

Tripti Goel, M. Tanveer, Amir Hussain, Zain Hussain

• Publisher: Academic Press
• Year: 2026
• Topic: Biological Sciences
• eBook ISBN:
  9780443302923
• Subtopic: Biotechnology
• Index: Biological Sciences

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. - Introduces Deep Learning structures and pre-processing methods for detecting MRI anomalies - Discusses current research issues and future research objectives - Compares a new method using open-access benchmark datasets using Deep Learning on MRI images to detect and classify Alzheimer's Disease, Parkinson's Disease, and Schizophrenia

Table of contents

• Title of Book
• Chapter 1: Graph convolutional networks for diagnosis of Alzheimer's disease: a review
• Chapter 2: Deep learning in neurological disease and brain health: foundations, applications, and challenges
• Chapter 3: Deep learning in brain tumors
• Chapter 4: Deep learning in Parkinson's disease
• Chapter 5: Predicting biological age of the brain using magnetic resonance imaging and deep learning methods
• Chapter 6: Artificial intelligence for schizophrenia detection: a comparative review of machine learning and deep learning models
• Chapter 7: Alzheimer's disease diagnosis using optimized ensemble deep learning model
• Chapter 8: A framework for characterizing brain iron concentration in neurodegenerative disease using quantitative susceptibility mapping
• Chapter 9: Deep learning for ischemic stroke lesion segmentation: towards a unified approach
• Chapter 10: Advanced entropy feature engineering using EMD and VMD for schizophrenia classification
• Chapter 11: Issues and challenges of neurological disorders diagnosis research
• Chapter 12: Opportunities and future directions of neurological disorders diagnosis research
• Chapter 13: Neurology beyond deep learning
• Subject index