Lightweight, Real-Time Deep Learning Models for Healthcare Applications

Erscheinungsjahr 2026
ISBN: 978-0-443-45426-4
Verlag: Elsevier Science

Buch, Englisch, 300 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

Lightweight, Real-time Deep Learning Models for Healthcare Applications addresses the pressing need for deploying efficient artificial intelligence models in clinical environments constrained by limited computational resources. As AI adoption accelerates in healthcare, this reference targets the gap between advanced deep learning research and practical real-time applications, emphasizing model optimization, privacy, and regulatory compliance. The content comprehensively covers model compression techniques such as pruning, quantization, knowledge distillation, and neural architecture search, alongside federated learning frameworks and real-time inference optimization. Detailed chapters present hardware-aware strategies, security considerations, continuous learning, and regulatory validation. The book also provides extensive case studies and implementation blueprints demonstrating practical deployment on devices like Raspberry Pi, NVIDIA Jetson, and mobile health platforms. This book benefits researchers, clinicians, and healthcare technology professionals by offering practical solutions to develop, validate, and implement AI models that maintain diagnostic accuracy while meeting clinical constraints. It equips students and educators with theoretical foundations and hands-on examples to foster innovation in biomedical AI, ultimately enhancing diagnostic capabilities and patient care in diverse healthcare settings.

Autoren/Hrsg.

Babu, Tina

Nair, Rekha R

Balusamy, Balamurugan

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie

Inhaltsverzeichnis

Introduction: Foundations of Deep Learning for Healthcare Applications
1. Magnitude-Based Neural Network Pruning for Medical Imaging
2. Post-Training Quantization for Point-of-Care Diagnostic Systems
3. Knowledge Distillation from Ensemble Models to Single-Shot Detectors
4. Dynamic Neural Architecture Search for Adaptive Clinical Workloads
5. Federated Learning with Differential Privacy in Multi-Hospital Networks
6. Real-Time Inference Optimization for Critical Care Monitoring
7. Edge-Cloud Hybrid Architectures for Telemedicine Applications
8. Memory-Efficient Training Strategies for Medical Deep Learning
9. Model Compression via Tensor Decomposition for Wearable Health Devices
10. Automated Model Selection and Hyperparameter Optimization for Clinical AI
11. Interpretable Lightweight Models for Clinical Decision Support
12. Continuous Learning and Model Updates in Deployed Healthcare Systems
13. Security and Adversarial Robustness in Lightweight Medical AI
14. Regulatory Compliance and Validation Frameworks for Optimized Medical AI
15. Case Studies and Implementation Blueprints for Healthcare AI Deployment
16. Cost-Benefit and ROI Analysis of Lightweight AI in Global Healthcare Settings

Über Autor(innen)

Nair, Rekha R
Dr. Rekha R. Nair is a seasoned academic and researcher with extensive experience in computer science, specializing in medical image processing, machine learning, data science, and artificial intelligence. She currently serves as an Assistant Professor in the Department of Computer Science and Engineering at Alliance University, Bengaluru. Previously, she was a faculty member at Dayananda Sagar University and completed her Ph.D. at Amrita Vishwa Vidyapeetham, where her research focused on developing algorithms for multi-modal medical image fusion. Over the years, Dr. Nair has taught a variety of computer science courses, such as Artificial Intelligence, Data Science, and Blockchain Technology, while actively contributing to curriculum development and department administration. Dr. Nair's research endeavors are well-recognized, with numerous publications in top-tier journals and conferences. Her work includes pioneering efforts in medical image fusion and cancer detection using deep learning frameworks. She has collaborated on projects addressing real-world problems, such as IoT-driven energy conservation and academic integrity using AI models. Notably, she has contributed to various book chapters and holds patents for innovative technological solutions. In addition to her scholarly achievements, Dr. Nair actively participates in faculty development programs and serves as a reviewer for several prestigious scientific journals, enhancing her contributions to the research community.

Balusamy, Balamurugan
Dr. Balamurugan Balusamy is currently working as an Associate Dean Student in Shiv Nadar Institution of Eminence, Delhi-NCR. He is part of the Top 2% Scientists Worldwide 2023 by Stanford University in the area of Data Science/AI/ML. He is also an Adjunct Professor in the Department of Computer Science and Information Engineering, Taylor University, Malaysia. His contributions focus on engineering education, blockchain, and data sciences

Babu, Tina
Dr. Tina Babu is an accomplished engineering educator and researcher currently serving as Assistant Professor in the Department of Computer Science and Engineering at Alliance University, Bengaluru. With over 11 years of academic experience, she specializes in image processing, artificial intelligence, machine learning, and pattern recognition. Dr. Babu holds a Ph.D. focused on automated colon cancer screening and grading of histopathological images, demonstrating her expertise in applying AI to medical diagnostics. Her research contributions include over 50 publications in prestigious journals and conferences, with particular emphasis on medical image analysis and deep learning applications. She has also secured multiple patents and received research funding from SERB. Dr. Babu maintains an impressive academic profile with an h-index of 10. Her teaching portfolio encompasses advanced courses in Computer Vision, Machine Learning, and Database Management Systems, reflecting her commitment to nurturing the next generation of technology professionals.