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Elsevier

Multimodal Deep Learning and Data-Centric Systems for Smart Healthcare and Clinical Decision Support

  • 1st Edition - December 1, 2026
  • Latest edition
  • Editors: Manoj Diwakar, Prabhishek Singh, Sweta Sneha, Akbar Sheikh-Akbari
  • Language: English

Multimodal Deep Learning and Data-Centric Systems for Smart Healthcare and Clinical Decision Support examines how to fuse imaging, genomics, electronic health records, and wearab… Read more

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Description

Multimodal Deep Learning and Data-Centric Systems for Smart Healthcare and Clinical Decision Support examines how to fuse imaging, genomics, electronic health records, and wearable sensor data into clinically actionable insights. As healthcare data becomes increasingly diverse and voluminous, there is a pressing need for integrative methodologies that preserve information across modalities while maintaining interpretability and safety. Current resources either focus on single-modality AI or domain-specific applications, leaving practitioners with fragmented guidance. This volume defines a cohesive, data-centric framework for multimodal predictive diagnostics and clinical decision support, addressing methodological foundations, reproducible pipelines, and real-world translation challenges.

Key features

  • Integrates multimodal AI across imaging, genomics, and clinical data
  • Presents case studies on predictive diagnostics and decision support
  • Addresses explainability, fairness, and regulatory compliance challenges

Readership

Biomedical informatics researchers and and computer scientists who focus on AI applications in healthcare data

Table of contents

1. Data-Centric Multimodal Clinical Decision Support: Curation, Harmonization, and Evaluation

2. Explainable Ensemble Learning for Multiclass Diabetic Retinopathy Classification Using Retinal Fundus Images

3. A Multimodal Deep Learning Framework for Pulmonary Disease Prediction Using Medical Imaging

4. Neurological Disease Forecasting from Brain Imaging Using Multimodal Deep Learning Frameworks

5. Edge Computing for Real-Time Health Monitoring of Obstructive Sleep Apnea

6. AI-Driven Multimodal Digital Phenotyping for Passive Mental Health Monitoring and Pre-emptive Support Strategies: A Modular Approach

7. Integrating Multimodal Data for Early Cancer Detection: An AI-Driven Approach

8. Pneumonia Detection from Chest X-rays Using Multimodal Feature Fusion and Deep Learning Models

9. Early Cancer Detection Using Multimodal Artificial Intelligence: A Transcriptomic Learning Approach

10. Performance Analysis of Quantum Machine Learning Models for Imbalanced ECG Arrhythmia Detection

11. A Multimodal Artificial Intelligence Framework for Early Cancer Detection through Integrated Clinical, Imaging, and Molecular Data

12. Infectious Disease Surveillance Using Imaging and Clinical Data

13. A Data-Centric Comparative Study of Classical Machine Learning and Hybrid CNN-LSTM Models for Intelligent Clinical Decision Support in Smart Healthcare Systems

14. Early Cancer Detection Using Multimodal AI

15. A Stack Ensemble Learning Model for Parkinson’s Disease Detection Using Support Vector Machine, Decision Tree, and XGBOOST

16. Multimodal Brain Tumor Analysis Using Pixel-Level MRI–CT Fusion and Quantitative Evaluation

17. Exploring the potential of artificial intelligence and deep learning in medical imaging for automating image interpretation, providing diagnostic assistance and enabling personalized treatment

18. Multiple Disease Detection Model Using Hybrid Machine Learning and Deep Learning Architectures

Product details

  • Edition: 1
  • Latest edition
  • Published: December 1, 2026
  • Language: English

About the editors

MD

Manoj Diwakar

Dr. Manoj Diwakar is currently working as Associate professor in the Department of Computer Science and Engineering at Graphic Era Deemed to be University, Dehradun. With more than a decade of industrial and academic experience, he is committed and dedicated to the continuous upliftment of the research environment in the department. His research interests include Image Processing, Information Security and Medical Imaging. He has published more than 110 research papers in peer-reviewed journals, conferences, books and book chapters with national and international publishers of repute. He has also served as Guest editors of many reputed journals. He organized many international conferences. He has served as Associate editors/Editorial members of many reputed journals .

Affiliations and expertise
Associate Professor, Department of Computer Science and Engineering, Graphic Era Deemed to be University, India

PS

Prabhishek Singh

Dr. Prabhishek Singh is working (Senior IEEE Member) as an Assistant Professor in School of Computer Science Engineering and Technology, Bennett University (Times of India Group), Greater Noida, India since 2022. He has total teaching and research experience of 8 years. He did his Ph.D. in 2018. He did his M. Tech in 2013, and B.Tech in 2010. He is also awarded with young scientist award and excellent researcher award. He has published 100+ research papers in SCI/SCIE/Scopus, ESCI journals, and conferences. His research interest includes Image Processing and Computer Vision, Deep Learning, and Machine Learning. He is serving as an Associate Editor, Academic Editor, Review Editor, Guest Editor, Reviewer, and Editorial Committee Chair of many SCI/SCIE/Scopus and ESCI journals, and other prestigious conferences.

Affiliations and expertise
Assistant Professor, School of Computer Science Engineering and Technology, Bennett University, India

SS

Sweta Sneha

Sweta Sneha works in the Michael J. Coles College of Business at Kennesaw State University, USA.
Affiliations and expertise
Michael J. Coles College of Business, Kennesaw State University, USA

AS

Akbar Sheikh-Akbari

Dr Akbar Sheikh-Akbari is a Reader (Associate Professor) in the School of Built Environment, Engineering and Computing at Leeds Beckett University. He holds a PhD in Electronic and Electrical Engineering from the University of Strathclyde. His research focuses on biometric identification, hyperspectral imaging, colour processing, image super-resolution, multiview image/video systems, deep learning, and artificial intelligence. Dr. Sheikh-Akbari has published over 100 peer-reviewed papers and has led several funded projects, including recent work on hyperspectral imaging for aflatoxin detection and RFID-based asset management. He has also supervised ten PhD and two MRes researchers to successful completion.
Affiliations and expertise
Associate Professor, School of Built Environment, Engineering and Computing, Leeds Beckett University, UK