Deep Learning in Internet of Things for Next Generation Healthcare

✍ Scribed by Lavanya Sharma (editor), Pradeep Kumar Garg (editor)

• Publisher: Chapman and Hall/CRC
• Year: 2024
• Tongue: English
• Leaves: 288
• Edition: 1
• Category: Library

✦ Synopsis

This book presents the latest developments in deep learning-enabled healthcare tools and technologies and offers practical ideas for using the IoT with deep learning (motion-based object data) to deal with human dynamics and challenges including critical application domains, technologies, medical imaging, drug discovery, insurance fraud detection and solutions to handle relevant challenges. This book covers real-time healthcare applications, novel solutions, current open challenges, and the future of deep learning for next-generation healthcare. It includes detailed analysis of the utilization of the IoT with deep learning and its underlying technologies in critical application areas of emergency departments such as drug discovery, medical imaging, fraud detection, Alzheimer's disease, and genomes.

• Presents practical approaches of using the IoT with deep learning vision and how it deals with human dynamics
• Offers novel solution for medical imaging including skin lesion detection, cancer detection, enhancement techniques for MRI images, automated disease prediction, fraud detection, genomes, and many more
• Includes the latest technological advances in the IoT and deep learning with their implementations in healthcare
• Combines deep learning and analysis in the unified framework to understand both IoT and deep learning applications
• Covers the challenging issues related to data collection by sensors, detection and tracking of moving objects and solutions to handle relevant challenges

Postgraduate students and researchers in the departments of computer science, working in the areas of the IoT, deep learning, machine learning, image processing, big data, cloud computing, and remote sensing will find this book useful.

✦ Table of Contents

Cover
Half Title
Title
Copyright
Dedication
Table of Contents
Preface
Editor Biographies
List of Contributors
Chapter 1 Rise of Communication Devices in IoT
1.1 Introduction
1.2 Internet of Things
1.3 Existing Scenario of Communication Devices in IoT Systems
1.4 Emerging Communication Devices in IoT Systems
1.5 Conclusion
References
Chapter 2 Architecture Framework for Deep Learning Systems and IoT: An Overview
2.1 Introduction
2.2 Architecture Framework for IoT
2.3 Architecture Framework for Deep Learning Systems
2.4 Applications
2.4.1 Deep Learning Systems
2.4.2 IoT
2.5 Conclusions and Future Scope
References
Chapter 3 Deep Learning and Human Vision in IoT
3.1 Introduction
3.1.1 Why Is Classical Machine Learning Less Effective Than Deep Learning?
3.2 Importance of Human Vision in IoT Systems
3.3 Challenges and Opportunities in Combining Deep Learning and Human Vision for IoT
3.3.1 Challenges
3.3.2 Opportunities
3.4 Material Science and the IoT
3.5 Visual Perception Processes
3.5.1 Emerging Trends in Deep Learning and Human Vision in the IoT
3.5.2 IoT Technologies for Sustainable Development
References
Chapter 4 Impact of IoT on Big Data Analytics and Applications in Medical Images
4.1 Introduction
4.2 Techniques and Application Used in IoT
4.2.1 Technological Aspects of the IoT
4.2.2 IoT Connectivity
4.2.3 Application of the IoT
4.2.4 IoT Deployment
4.3 Big Data
4.3.1 Big Data Analytics
4.4 Application of Big Data
4.5 Big Data in the IoT
4.5.1 IoT Big Data Processing
4.5.2 Applications of Big Data Integrated with the IoT
4.6 Impact of IoT and Big Data in Medical Images Using Deep Learning
4.7 Challenges of the Impact of the IoT in Big Data
4.8 Conclusion
References
Chapter 5 Geospatial Data Collection Tools in Healthcare
5.1 Introduction
5.2 Geospatial Data Collection Devices
5.2.1 Digitization
5.2.2 Global Positioning Systems
5.2.3 Mobile Technology
5.2.4 Remote Sensing
5.2.5 Sensors
5.2.6 Social Media
5.3 Geospatial Data Analysis
5.4 Application Areas of Geospatial Data
5.5 Future Scope
References
Chapter 6 Geospatial Technology in Healthcare
6.1 Introduction
6.1.1 The Evolution of the Geospatial Sector
6.1.2 The Rise of GIS Application in Healthcare
6.1.3 The Role of the Geospatial Sector in Aiding the Delivery of Healthcare Services
6.1.4 Challenges and Opportunities in Healthcare GIS
6.1.5 Action Plan and the Way Forward
6.2 The Evolution of the Geospatial Sector
6.3 The Rise of GIS Applications in Healthcare
6.4 The Role of the Geospatial Sector in Aiding the Delivery of Healthcare Services
6.4.1 Aayushman Bharat Digital Health Mission
6.4.2 Mapping Burden of Diseases
6.4.3 AarogyaSetu—A Digital Initiative to Fight the Pandemic by Leveraging GIS Technology
6.5 Challenges and Opportunities in Healthcare GIS
6.6 Action Plan and the Way Forward
References
Chapter 7 Advancement of Geospatial Technology in Healthcare Systems
7.1 Introduction to Geospatial Technologies
7.2 Application of Geospatial Technology in Public Health Systems
7.3 Advancements in Geospatial Technologies in Private Healthcare Systems
7.4 Challenges in Application of Geospatial Technologies in Healthcare Systems
7.5 Future of Geospatial Technologies in Healthcare Systems
References
Chapter 8 Implementation of Deep Learning in Assessment of Health-Hazardous Air Pollutants
8.1 Introduction
8.2 Air Pollution and Its Impact on Health
8.3 Health Hazardous Pollutants
8.3.1 Particulate Matter
8.3.2 Sulfur Dioxide
8.3.3 Oxides of Nitrogen
8.3.4 Ammonia
8.3.5 Carbon Monoxide
8.3.6 Ozone
8.3.7 Benzene
8.3.8 Toluene
8.3.9 Xylene
8.3.10 Arsenic
8.3.11 Nickel
8.4 New Trends in Computing
8.4.1 Artificial Intelligence
8.4.2 Machine Learning
8.4.3 Deep Learning
8.5 Application of AI/ML/DL in Estimation of Health-Hazardous Pollutants
8.6 Conclusions
References
Chapter 9 Technological Interventions in Healthcare
9.1 Introduction
9.1.1 Diagnostics and Sample Transportation
9.1.2 Emergency Medical Services
9.1.3 Telemedicine and Remote Patient Monitoring
9.1.4 Challenges and Regulatory Considerations
9.2 Diagnostics and Sample Transportation
9.2.1 Types of Diagnostic Samples Transported by Drones
9.2.2 Benefits of Drone-Based Diagnostics and Sample Transportation
9.2.3 Challenges and Limitations of Drone-Based Diagnostics and Sample Transportation
9.2.4 Impact on the Indian Healthcare System
9.3 Emergency Medical Services
9.3.1 Challenges and Limitations of the Current EMS System
9.3.2 Potential Impact of Technology on EMS
9.3.3 Future of EMS in India
9.4 Telemedicine and Remote Patient Monitoring
9.4.1 Benefits of Telemedicine and Remote Patient Monitoring
9.4.2 Challenges of Telemedicine and Remote Patient Monitoring
9.4.3 Role of Technology in Telemedicine and Remote Patient Monitoring
9.4.4 Future of Telemedicine and Remote Patient Monitoring in India
9.5 The Role of Public–Private Partnerships in Advancing Healthcare in India
9.5.1 Benefits of Public–Private Partnerships in Healthcare
9.5.2 Challenges of Public–Private Partnerships in Healthcare
9.5.3 Role of Technology in Public–Private Partnerships
9.5.4 Future of Public–Private Partnerships in Advancing Healthcare in India
9.6 Challenges and Regulatory Considerations
9.6.1 Challenges in the Indian Healthcare System
9.6.2 Regulatory Considerations in the Indian Healthcare System
9.6.3 Addressing Challenges and Regulatory Considerations
9.7 Conclusion
References
Chapter 10 Disaster and Emergency Healthcare
10.1 Disaster
10.2 Healthcare
10.3 Emergency Healthcare
10.4 Disaster Management Cycle
10.5 Implementing Health Emergency and Disaster Risk Management
10.6 Latest Technological Advancements in Emergency Healthcare
References
Chapter 11 Deep Learning and IoT in Healthcare
11.1 Introduction
11.2 Big Data: Concept and Definition
11.2.1 Big Data Engineering
11.2.2 Non-Relational Model
11.2.3 NoSQL
11.2.4 Big Data Models
11.2.5 Schema-on-Read
11.2.6 Big Data Analytics
11.2.7 Big Data Paradigm
11.3 Using the Cloud for Data Management
11.4 Managing Big Data in Environments of Cloud Computing
11.5 Solutions and Techniques for Data Storage
11.6 Big Data Frameworks
11.6.1 Hadoop
11.6.2 MapReduce
11.6.3 Spark
11.6.4 Hive
11.6.5 Storm
11.6.6 Flink
11.6.7 Heron
11.6.8 NoSQL Databases
11.6.9 Challenges in the Visualisation of NoSQL Databases
11.7 Advantages of Big Data Applications
11.8 Factors of Big Data Frameworks
11.8.1 Processing Speed
11.8.2 Fault Tolerance
11.8.3 Scalability
11.8.4 Security
11.9 Advantages of Big Data and Cloud Computing Frameworks
11.10 Challenges and Risks of Big Data and Cloud Computing Frameworks
11.11 Revolutionising Healthcare
11.11.1 The Role of Big Data in Empowering Deep Learning
11.11.2 Harnessing Cloud Computing for Data Storage and Processing
11.11.3 IoT Devices: Augmenting Healthcare Data Collection
11.11.4 Personalised Medicine and Customised Treatment Strategies
References
Chapter 12 Improved Patient Care Using Robotics in the Healthcare Industry: Benefits, Real-Time Applications, and Challenges
12.1 Introduction
12.2 Major Benefits of Robotics in the Healthcare Industry
12.2.1 High-End Healthcare
12.2.2 Safer Work Environment
12.2.3 Simplified Hospital Workflows
12.2.4 Surgical Robots in Operating Theatres
12.3 Examples of Robotics
12.3.1 da Vinci Surgical Robots
12.3.2 Capsule Endoscope Robots
12.3.3 Orthoses (a.k.a. Exoskeletons)
12.3.4 Disinfectant Robots
12.3.5 Companion Robots
12.3.6 Robotic Nurses
12.3.7 Robotic-Assisted Biopsy
12.3.8 Antibacterial Nanorobots
12.4 Challenging Issues in Adopting Robotics
12.5 Future of Robotics
12.6 Conclusion
References
Chapter 13 Deep Learning Processes in MRI Images
13.1 Introduction
13.2 Processing of MRI Images
13.2.1 Preprocessing
13.2.2 Segmentation
13.2.3 Classification
13.3 MRI Image Processing Using Deep Learning Techniques
13.3.1 Input Layer
13.3.2 Hidden Layer
13.3.3 Output Layer
13.4 Deep Learning Applications in MRI Images
13.4.1 Pre-Processing of MRI Images Using Deep Learning
13.4.2 MRI Image Segmentation Using Deep Learning
13.4.3 MRI Image Classification Using Deep Learning
13.5 Application of Deep Learning in MRI Image Preprocessing
13.6 Conclusion
References
Chapter 14 Artificial Intelligence and Robotics in Healthcare: Transforming the Indian Landscape
14.1 Introduction
14.1.1 Factors Contributing to the Growing Interest in AI and Robotics in Indian Healthcare
14.1.2 Key Players in the Indian AI and Robotics Healthcare Ecosystem
14.1.3 Potential Impact of AI and Robotics on Indian Healthcare
14.2 The Emergence of AI and Robotics in Indian Healthcare
14.2.1 Need for Cost-Effective Solutions
14.2.2 Rise of Digital Health
14.2.3 Increasing Prevalence of Chronic Diseases
14.2.4 Government Initiatives
14.2.5 Key Players in the Indian AI and Robotics Healthcare Ecosystem
14.3 AI and Robotics Applications in Indian Healthcare
14.3.1 Diagnostics
14.3.2 Treatment
14.3.3 Patient Care
14.3.4 Research
14.4 Challenges and Ethical Considerations
14.4.1 Challenges
14.4.2 Ethical Considerations
14.5 The Future of AI and Robotics in Indian Healthcare
14.5.1 Key Trends and Developments
14.5.2 Emerging Applications
14.5.3 Potential Impact on Indian Healthcare
14.6 Conclusion
References
Chapter 15 Medical Insurance Fraud Detection
15.1 Medical Insurance: Introduction and Its Benefits
15.2 Medical Insurance Fraud
15.3 Types of Medical Insurance Fraud
15.3.1 Fraud by Service Providers
15.3.2 Fraud by Subscribers
15.3.3 Fraud by Insurance Carriers
15.3.4 Conspiracy Fraud
15.4 Traditional Methods of Medical Insurance Fraud Detection
15.4.1 Auditing
15.4.2 Whistleblowing
15.4.3 Manual Review of Claims
15.5 Technological Methods of Medical Insurance Fraud Detection
15.6 Use of Technologies in Mitigating the Challenges Identified
15.7 Role of Laws, Regulations, and Policy Measures in Fraud Detection
15.8 Future Trends and Challenges
15.9 Conclusion and Way Forward
References
Chapter 16 Privacy and Security Issues for IoT and Deep Learning in Next-Generation Healthcare: An Indian Perspective
16.1 Introduction
16.1.1 The Indian Healthcare Landscape
16.1.2 IoT and Deep Learning in Healthcare
16.1.3 Privacy and Security Concerns
16.1.4 Addressing Privacy and Security Challenges
16.1.5 The Way Forward
16.2 The Indian Healthcare Landscape
16.2.1 Public Healthcare System
16.2.2 Challenges in Public Healthcare Systems
16.2.3 Private Healthcare System
16.2.4 Rural–Urban Divide
16.2.5 Role of Technology in Indian Healthcare
16.2.6 Opportunities for IoT and Deep Learning in Indian Healthcare
16.3 IoT and Deep Learning in Healthcare
16.3.1 IoT in Healthcare
16.3.2 Deep Learning in Healthcare
16.3.3 Integration of IoT and Deep Learning in Healthcare
16.3.4 Challenges and Barriers to Adoption
16.4 Privacy and Security Concerns
16.4.1 Data Privacy Concerns
16.4.2 Data Security Concerns
16.4.3 Regulatory Landscape
16.4.4 Strategies for Addressing Privacy and Security Concerns
16.5 Addressing Privacy and Security Challenges
16.5.1 Technological Solutions
16.5.2 Policy Development
16.5.3 Collaboration among Stakeholders
16.5.4 Education and Training
16.5.5 Continuous Improvement and Adaptation
16.5.6 Legal and Regulatory Considerations
16.6 The Way Forward
16.6.1 Future Trends in Healthcare IoT and Deep Learning
16.6.2 Emerging Technologies and Their Impact on Privacy and Security
16.6.3 Strategies for Navigating the Evolving Privacy and Security Landscape
16.7 Conclusion
References
Chapter 17 A Systematic Review on the Future of Internet of Things Applications in Healthcare
17.1 Introduction
17.2 Literature Review
17.3 Literature Summary
17.4 Conclusion
References
Chapter 18 The Extraordinary Importance of 6G Network Development and 3D Holography in Future Healthcare
18.1 Introduction
18.2 6G Technology
18.2.1 Background
18.2.2 Edge Technology
18.3 Holographic Communication
18.3.1 History of Holography and Development
18.3.2 Hologram Recording
18.3.3 Reconstruction of the Hologram
18.3.4 Holography and Artificial Intelligence
18.4 Augmented and Virtual Reality
18.5 Tactile/Haptic Internet
18.6 Intelligent Internet of Medical Things
18.7 Telesurgery, Epidemics and Pandemics and Precision Medicine
18.8 The Metaverse and Holographic Simulation
18.9 Conclusion
References
Chapter 19 Tracking of Disease—A Review of the State of the Art of Technology for Next Generation Healthcare
19.1 Introduction
19.2 Approaches for Tracking of Disease
19.2.1 Conventional Tracking of Disease
19.2.2 Sustainable Tracking of Disease
19.2.3 Methods of Tracking of Disease
19.3 Role of the IoT in Tracking of Disease
19.3.1 IoT-Enabled Wearable Devices for Health Monitoring
19.3.2 IoT in Environmental Monitoring for Prediction of Disease Outbreak
19.3.3 IoT-Based Predictive Analysis for Tracking of Disease
19.4 Deep Learning Techniques for Tracking of Disease
19.4.1 Deep Learning Algorithms Used in Tracking of Disease
19.4.2 Applications of Deep Learning in Tracking of Disease
19.5 Integration of IoT and Deep Learning in Tracking of Disease
19.5.1 Leveraging IoT Data for Deep Learning Models
19.5.2 Real-Time Tracking of Disease and Early Warning Systems
19.5.3 Data Fusion and Integration for Enhanced Tracking of Disease
19.6 Challenges and Future Directions
19.6.1 Ethical Considerations
19.6.2 Scalability and Interoperability Challenges
19.6.3 Newly Emerging Diseases
19.6.4 Emerging Trends and Future Directions in Tracking of Disease
19.7 Conclusion
References
Chapter 20 Disease Detection Using TensorFlow Methodology
20.1 Introduction
20.2 Objective
20.3 Data, Algorithms, and Methods
20.4 Methodology
20.4.1 Data Processing System
20.4.2 Data Architecture Using Machine Learning Techniques
20.4.3 Calculating Feature Importance
20.4.4 Training and Validation Loss Curves
20.5 Conclusion
References
Chapter 21 AI and Deep Learning: Applications in Healthcare
21.1 Introduction
21.2 Understanding AI, Machine Learning, and Deep Learning in Healthcare
21.2.1 Scopes of Applying AI in Healthcare
21.2.2 Real-Time Case Studies
21.3 Challenges and Opportunities
21.4 Future Trends
21.5 Conclusion
References
Index