Multimodal Deep Learning and Data-Centric Systems for Smart Healthcare and Clinical Decision Support: (Intelligent Data-Centric Systems)

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.

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

About the Author :
Dr. Manoj Diwakar is Associate Professor in the Department of Computer Science and Engineering at Graphic Era Deemed to be University, Dehradun, India. His research interests include image processing, information security, and medical imaging. He has contributed to research published in peer-reviewed journals, conference proceedings, books, and book chapters. Dr. Diwakar has served as a guest editor for scholarly journals and as a member of editorial boards in his areas of expertise. He has also been involved in organizing and supporting international conferences and research-focused academic initiatives. His work focuses on advancing interdisciplinary research and applications in computing and imaging technologies. Dr. Prabhishek Singh is Assistant Professor in the School of Computer Science Engineering and Technology at Bennett University, Greater Noida, India, where he has served since 2022. His academic and research activities focus on image processing, computer vision, machine learning, and deep learning. Dr. Singh serves as a Senior Member of IEEE and contributes to the scholarly community through editorial and peer-review roles for a range of journals and conferences. His professional service includes appointments as Associate Editor, Academic Editor, Review Editor, Guest Editor, Reviewer, and Editorial Committee Chair. His research interests encompass the development and application of computational methods for intelligent image analysis and data-driven systems. Dr. Sweta Sneha is Dean of the Wright School of Business and Sesquicentennial Endowed Chair at Dalton State College. Previously, she served on the faculty at Kennesaw State University and founded its interdisciplinary Master of Science program in Healthcare Management and Informatics. Her work focuses on health informatics and healthcare management. Dr. Akbar Sheikh-Akbari is Reader (Associate Professor) in the School of Built Environment, Engineering and Computing at Leeds Beckett University, where he has been a faculty member since 2015. He received his PhD in Electronic and Electrical Engineering from the University of Strathclyde and subsequently held research and academic positions at the University of Bristol, Staffordshire University, and the University of Gloucestershire. His research focuses on biometric identification, hyperspectral image processing, image and video analysis, image super-resolution, multimedia coding, assisted living technologies, deep learning, and artificial intelligence. He has contributed to research projects in areas including multiview video processing, intelligent monitoring systems, RFID-based asset management, and hyperspectral imaging for food quality and safety. He serves as principal investigator and academic lead on industry-collaborative research and innovation projects funded through Knowledge Transfer Partnerships and Innovate UK initiatives.