Data Fusion Techniques and Applications for Smart Healthcare

1st Edition - March 12, 2024
Editors: Amit Kumar Singh, Stefano Berretti
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 3 2 3 3 - 9
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 3 2 3 4 - 6

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 algori… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
List of contributors
Preface
1. Introduction
2. Summary of book chapters
3. Conclusions
Chapter 1: Retinopathy screening from OCT imagery via deep learning
Abstract
1.1. Introduction
1.2. Retinal OCT classification framework
1.3. Simulation results
1.4. Discussion
1.5. Conclusion and future scope
References
Chapter 2: Multisensor data fusion in Digital Twins for smart healthcare
Abstract
2.1. Introduction
2.2. Literature review
2.3. SVM optimization
2.4. DSET analysis
2.5. Construction of a multisensor medical data decision-level fusion DTs model based on the combination of improved SVM and DSET
2.6. Experimental verification
2.7. Results
2.8. Discussion
2.9. Conclusion
References
Chapter 3: Deep learning for multisource medical information processing
Abstract
3.1. Introduction and motivation
3.2. Background, definitions, and notations
3.3. Literature review and state-of-the-art
3.4. Problem definition
3.5. Proposed solution
3.6. Challenges and prospective opinion
3.7. Conclusion
References
Chapter 4: Robust watermarking algorithm based on multimodal medical image fusion
Abstract
Acknowledgements
4.1. Introduction
4.2. Literature survey
4.3. Background information
4.4. Proposed methodology
4.5. Results
4.6. Conclusion
References
Chapter 5: Fusion-based robust and secure watermarking method for e-healthcare applications
Abstract
Acknowledgement
5.1. Introduction
5.2. Literature review
5.3. Proposed method
5.4. Results and analysis
5.5. Conclusion
References
Chapter 6: Recent advancements in deep learning-based remote photoplethysmography methods
Abstract
6.1. Introduction
6.2. Photoplethysmography
6.3. Remote photoplethysmography methods
6.4. Limitations and recommendations for future research
6.5. Conclusion
References
Chapter 7: Federated learning in healthcare applications
Abstract
7.1. Introduction
7.2. Preliminaries
7.3. FL applications in healthcare
7.4. Challenges and considerations
7.5. Conclusions and future scope
References
Chapter 8: Riemannian deep feature fusion with autoencoder for MEG depression classification in smart healthcare applications
Abstract
8.1. Introduction and motivation
8.2. Literature review and state-of-the-art
8.3. Problem definition
8.4. Proposed solution
8.5. Experiment
8.6. Conclusion and future work
References
Chapter 9: Source localization of epileptiform MEG activity towards intelligent smart healthcare: a retrospective study
Abstract
9.1. Introduction and motivation
9.2. Literature review and state-of-the-art
9.3. MEG recordings
9.4. MRI
9.5. Problem definition
9.6. Dataset description
9.7. Proposed solution
9.8. Results and discussion
9.9. Conclusion and future work
References
Chapter 10: Early classification of time series data: overview, challenges, and opportunities
Abstract
10.1. Introduction
10.2. Overview
10.3. Methods
10.4. Data fusion
10.5. Challenges
10.6. Opportunities and future directions
10.7. Conclusion
References
Chapter 11: Deep learning-based multimodal medical image fusion
Abstract
11.1. Introduction
11.2. Literature survey and state-of-the-art
11.3. Proposed framework
11.4. Experimental results and discussion
11.5. Conclusion
References
Chapter 12: Data fusion in Internet of Medical Things: towards trust management, security, and privacy
Abstract
Acknowledgement
12.1. Introduction
12.2. Preliminaries of IoMT data fusion
12.3. Traditional data fusion methods
12.4. Data fusion in IoMT
12.5. Privacy-enhanced data fusion
12.6. Conclusion
References
Chapter 13: Feature fusion for medical data
Abstract
13.1. Introduction
13.2. Morphological methods
13.3. Component substitution-based methods
13.4. Multiscale decomposition-based methods
13.5. Deep learning methods for feature fusion
13.6. Fuzzy logic
13.7. Sparse representation methods
13.8. Feature fusion on medical data
13.9. Comparison of data fusion methods
13.10. Future works
13.11. Conclusion
References
Chapter 14: Review on hybrid feature selection and classification of microarray gene expression data
Abstract
14.1. Introduction and motivation
14.2. Background, definitions, and notations
14.3. System definition
14.4. Proposed solution
14.5. Experimental analysis
14.6. Conclusions
References
Chapter 15: MFFWmark: multifocus fusion-based image watermarking for telemedicine applications with BRISK feature authentication
Abstract
15.1. Introduction
15.2. MFFWmark: proposed technique
15.3. Simulation results and discussion
15.4. Comparison of results
15.5. Conclusion
References
Chapter 16: Distributed information fusion for secure healthcare
Abstract
16.1. Introduction
16.2. Background study
16.3. Methodology
16.4. Evaluation
16.5. Conclusion
References
Chapter 17: Deep learning for emotion recognition using physiological signals
Abstract
17.1. Introduction
17.2. Background and literature
17.3. Preliminaries
17.4. Proposed methodology
17.5. Experiments and results
17.6. Conclusions and future works
References
Index

Amit Kumar Singh

Amit Kumar Singh is an associate professor at the Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India. Dr. Singh have been recognized as "World Ranking of Top 2% Scientists" in the area of “Biomedical Research" (for Year 2019) and "Artificial Intelligence & Image Processing" (for the Year 2020 and 2021) according to the survey given by Stanford University, USA. Currently, Dr. Singh is the Associate Editor of IEEE Trans. on Multimedia, ACM Trans. Multimedia Comput. Commun. Appl., IEEE Trans. Computat. Social Syst., IEEE Trans. Ind. Informat., IEEE J. Biomed. Heal. Informatics Etc. His research interests include multimedia data hiding, image processing, compression, biometrics, Cryptography.

Affiliations and expertise

Associate Professor, Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India

Stefano Berretti

Prof. Berretti is an associate professor at the Media Integration and Communication Center (MICC) and Department of Information Engineering (DINFO) of the University of Florence (UNIFI), Florence, Italy. In 2017, he obtained habilitation as full professor in Computer Engineering. Prof. Berretti has worked on image databases for effective and efficient image retrieval based on color, shape attributes and spatial relationships. He also investigated the problem of retrieval from repositories distributed on the net using resource selection and results fusion. More recently, his research interests focused on deep learning methods for face recognition, and to their generalization to non-Euclidean domains (i.e., graphs, meshes, manifolds, etc.). He is information director and associate editor of the ACM Transactions on Multimedia Computing, Communication and Applications, and of the IET Computer Vision journal. He is a member of the ACM, and senior member of the IEEE.

Affiliations and expertise

Associate Professor, Department of Information Engineering, University of Florence(UNIFI), Florence, Italy.

Life sciences

Physical sciences & Engineering

Social sciences & Humanities

Health