Federated Learning for Neural Disorders in Healthcare 6.0

1. Auflage 2025
ISBN: 978-1-032-96887-2
Verlag: Taylor & Francis Ltd

Buch, Englisch, 414 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Future Generation Information Systems

This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimer’s disease using neural networks and ensuring data privacy and security in federated learning for neural disorders.

This book:

- Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disorders

- Focuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging research

- Examines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and security

- Explores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learning

- Offers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examples

It is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering.

Zielgruppe

Academic, Postgraduate, and Undergraduate Advanced

Autoren/Hrsg.

Nag, Anindya

Reddy C, Kishor Kumar

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Mathematik | Informatik EDV | Informatik Technische Informatik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe

Inhaltsverzeichnis

1. Federated Learning in Healthcare 6.0 Paradigm, Technologies and Challenges. 2. Evolving Neural Disorders in the Era of Healthcare 6.0: Classifications, Types, and Societal Impact. 3. Advancing Explainable AI in Healthcare Methods, Applications, and Ethical Implications. 4. From Neurons to Algorithms: Enhancing Machine Learning with Neuroscientific Insight. 5. Optimizing Neural Disorder Treatment through Federated Learning and Multi-Institutional Data Collaboration. 6. Harnessing Machine learning and deep learning techniques for neuroimaging. 7. AI and Federated Learning: Enhancing Cross-Institutional Research and Treatment Strategies for Neural Disorders. 8. Ensuring data privacy and security in federated learning for Healthcare data. 9. Federated Learning and Personalized Medicine: Tailoring Neural Disorder Therapies in Healthcare 6.0. 10. Federated Machine Learning And Augmented Reality (AR)/Virtual Reality (VR)-Based Framework For Schizophrenia Diagnosis And Therapy. 11. Harnessing Deep Learning for the Early Diagnosis of Dementia: A Transformative Approach in Neurological Health. 12. Federated Learning Based Diagnosis of Epilepsy Disease in Healthcare 6.0. 13. Early Detection of Alzheimer's: The Evolving Role of MRI in Neuroimaging. 14. Federated Learning-Enabled CNN for Predicting and Detecting Brain Tumors in Healthcare 6.0.

Über Autor(innen)

Kishor Kumar Reddy C is currently working as an associate professor in the Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, India. He has research and teaching experience of more than 13 years. He has published more than 90 research papers in national and international conferences, book chapters, and journals indexed by SCIE, Scopus and others. He has authored two textbooks and 12 edited books. He is a member of ISTE, CSI, IAENG, UACEE, and IACSIT.

Anindya Nag obtained an M.Sc. in Computer Science and Engineering from Khulna University in Khulna, Bangladesh, and a B.Tech. in Computer Science and Engineering from Adamas University in Kolkata, India. He is currently a lecturer in the Department of Computer Science and Engineering at the Northern University of Business and Technology in Khulna, Bangladesh. His research focuses on health informatics, medical Internet of Things, neuroscience, and machine learning. He serves as a reviewer for numerous prestigious journals and international conferences. He has authored and co-authored around 32 publications, including journal articles, conference papers, book chapters, and has co-edited five books.