Editors
- Raul Rodriguez, Vice-President, Woxsen University, India
- Hemachandran Kannan, Director AI Research Centre, Woxsen University, India
- Revathi Theerthagiri, Assistant Professor, Department of Analytics, School of Business, Woxsen University, India
- Khalid Shaikh, Founder and CEO, Prognica Labs, United Arab Emirates
- Sreelekshmi Bekal, Medical Director, Prognica Labs, United Arab Emirates
Introduction
"Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases," a groundbreaking book that explores the intersection of artificial intelligence and neuroscience. Neurodegenerative diseases, such as Alzheimer's and Parkinson's, pose significant challenges to global health, making early diagnosis vital for effective intervention. This comprehensive collection of chapters brings together leading experts in the fields of deep learning and neurology to showcase cutting-edge techniques and advancements in early detection. Through a fusion of innovative research and practical applications, this book seeks to pave the way for transformative solutions, revolutionizing the early diagnosis and management of neurodegenerative disorders.
Objective
The primary objective of "Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases" is to provide a comprehensive resource that explores the integration of deep learning methodologies with neuroscience for the early detection of neurodegenerative disorders. By assembling a diverse range of research contributions, this book aims to:
- Present state-of-the-art deep learning techniques tailored to neurodegenerative disease diagnosis.
- Bridge the gap between AI experts and neurologists, fostering interdisciplinary collaboration.
- Offer insights into the development of accurate, non-invasive, and cost-effective diagnostic tools.
- Showcase practical applications of deep learning in clinical settings, enhancing disease management.
- Contribute to current research by promoting novel approaches and potential breakthroughs, ultimately advancing the field of early diagnosis for neurodegenerative diseases.
Target Audience
The primary audience for this book includes researchers, clinicians, and professionals in the fields of neurology, artificial intelligence, machine learning, and biomedical engineering. It will also be beneficial for graduate students and postdoctoral researchers working on neurodegenerative diseases, as well as industry professionals interested in developing diagnostic tools and technologies. The book caters to individuals who seek a comprehensive understanding of the applications of deep learning in early disease detection and wish to contribute to advancements in the field. Additionally, policymakers and healthcare administrators focused on improving diagnostic practices and patient care for neurodegenerative disorders will find this publication informative and thought-provoking.
Recommended Topics
- Introduction to Neurodegenerative Diseases
- Fundamentals of Deep Learning
- Neuroimaging Data Acquisition and Preprocessing
- Feature Extraction and Representation Learning
- Deep Learning Models for Neurodegenerative Disease Diagnosis
- Integration of Multimodal Data for Enhanced Diagnosis
- Evaluation and Validation of Deep Learning Models
- Ethical Considerations and Challenges in Deep Learning for Neurodegenerative Diseases
- Translational Applications and Clinical Implementations
- Future Directions and Emerging Trends
- Case Studies
Publisher
This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints.
Submission of Full Paper: 25th October 2023
Acceptance of the Paper: 3rd Nobember 2023