Data Fusion Techniques and Applications for Smart Healthcare

Data Fusion Techniques and Applications for Smart Healthcare covers cutting-edge research from both academia and industry, with a particular emphasis on recent advances in algorithms and applications that involve combining multiple sources of medical information. The book can be used as a reference for practicing engineers, scientists, and researchers, but it will also be useful for graduate students and practitioners from government and industry as well as healthcare technology professionals working on state-of-the-art information fusion solutions for healthcare applications. Medical data exists in several formats, from structured data and medical reports to 1D signals, 2D images, 3D volumes, or even higher dimensional data such as temporal 3D sequences. Healthcare experts can make auscultation reports in text format; electrocardiograms can be printed in time series format, X-rays saved as images; volume can be provided through angiography; temporal information by echocardiograms, and 4D information extracted through flow MRI. Presents broad coverage of applied case studies using data fusion techniques to mine, organize, and interpret medical dataInvestigates how data fusion techniques offer a new solution for dealing with massive amounts of medical data coming from diverse sources and multiple formatsFocuses on identifying challenges, solutions, and new directions that will be useful for graduate students, researchers, and practitioners from government, academia, industry, and healthcare INDICE: Editors' Preface to Data Fusion Techniques and Applications for Smart Healthcare1. Retinopathy Screening from OCT Imagery via Deep Learning2. Multi-sensor data fusion in digital twins for smart healthcare3. Deep Learning for Multi-source Medical Information Processing4. Robust watermarking algorithm based on multimodal medical image fusion5. Fusion based Robust and Secure Watermarking Method for e-Healthcare Applications6. Recent Advancements in Deep Learning-based Remote Photoplethysmography Methods7. Federated Learning in Healthcare Applications8. Riemannian Deep Feature Fusion with auto-encoders for MEG Depression Classification in Smart Healthcare applications9. Epileptic Spike Localization using MEG MRI modality Fusion for Intelligent Smart Healthcare10. Early classification of time series data: Overview, Challenges, and Opportunities11. Deep Learning based multimodal medical image fusion12. Data fusion in internet of medical things: Towards trust management, security and privacy13. Feature fusion for medical data14. Review on Hybrid Feature Selection and Classification of Microarray Gene Expression Data15. MFFWmark: Multi focused fusion based image watermarking for telemedicine applications with BRISK feature authentication16. Distributed Information Fusion for Secured Healthcare17. Deep Learning for Emotion Recognition using Physiological Signals

• ISBN: 978-0-443-13233-9
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 442
• Fecha Publicación: 18/03/2024
• Nº Volúmenes: 1
• Idioma: Inglés