This book presents use-cases of IoT, AI and Machine Learning (ML) for healthcare delivery and medical devices. It compiles 15 topics that discuss the applications, opportunities, and future trends of machine intelligence in the medical domain. The objective of the book is to demonstrate how these technologies can be used to keep patients safe and healthy and, at the same time, to empower physicians to deliver superior care.
Readers will be familiarized with core principles, algorithms, protocols, emerging trends, security problems, and the latest concepts in e-healthcare services. It also includes a quick overview of deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, practical methodology, and how they can be used to provide better solutions to healthcare related issues. The book is a timely update for basic and advanced readers in medicine, biomedical engineering, and computer science.
Key topics covered in the book:
- An introduction to the concept of the Internet of Medical Things (IoMT)
- Cloud-edge based IoMT architecture and performance optimization in the context of Medical Big Data
- A comprehensive survey on different IoMT interference mitigation techniques for Wireless Body Area Networks (WBANs)
- Artificial Intelligence and the Internet of Medical Things
- A review of new machine learning and AI solutions in different medical areas.
- A Deep Learning based solution to optimize obstacle recognition for visually impaired patients
- A survey of the latest breakthroughs in Brain-Computer Interfaces and their applications
- Deep Learning for brain tumor detection
- Blockchain and patient data management
Author(s): Mariya Ouaissa, Mariyam Ouaissa, Zakaria Boulouad, Inam Ullah Khan, Sailesh Iyer
Series: Computational Intelligence for Data Analysis
Publisher: Bentham Science Publishers
Year: 2023
Language: English
Pages: 287
City: Singapore
Cover
Title
Copyright
End User License Agreement
Contents
Foreword
Preface
OBJECTIVE OF THE BOOK
ORGANIZATION OF THE BOOK
List of Contributors
Internet of Medical Things & Machine Intelligence
Health Services and Applications Powered by the Internet of Medical Things
Briska Jifrina Premnath1 and Namasivayam Nalini1,*
INTRODUCTION
CONCEPT FOR INTERNET-OF-THINGS-BASED HEALTHCARE
TECHNOLOGIES FOR HEALTHCARE SERVICE
Cloud Computing
Grid Computing
Big Data
Networks
Ambient Intelligence
Augmented Reality
Wearable
IOT'S HEALTHCARE BENEFITS
DIFFICULTIES IN IOMT
Confidentiality and Security of Data
Data Management
Scalability, Optimization, Regulation, and Standardization
Interoperability
Business Viability
Power Consumption
Environmental Consequences
SECURITY FOR THE INTERNET OF THINGS IN HEALTHCARE
Security Prerequisites
Security Challenges Memory Limitations
A Threat Model
Attack Types
Security Model Proposal
IOMT APPLICATIONS
Medical-Smart Technology
Ingestible Cameras
Monitoring of Patients in Real-Time is Number (RTPM)
System for Monitoring Cardiovascular Health
Skin Condition Monitoring Systems
Use of an IoMT Device as a Movement Detector
Wearable Sensors for Monitoring your Health from Afar
IOMT'S PART IN COVID-19
Technologies Collaborated with IoMT to Develop a Smart Healthcare System at COVID-19
Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
An Approach to the Internet of Medical Things(IoMT): IoMT-Enabled Devices, Issues, andChallenges in Cybersecurity
Internet of Medical Things in Cloud Edge Computing
G. Sumathi1,*, S. Rajesh2, R. Ananthakumar2 and K. Kartheeban2
INTRODUCTION
MEDICAL INTERNET OF THINGS
IOMT ARCHITECTURE
IOMT TECHNOLOGIES
Radio Frequency Identification (RFID)
Wireless Sensor Network (WSN)
MIDDLEWARE
IOMT APPLICATIONS
IOMT IN CLOUD
IOMT CLOUD ARCHITECTURE
HEALTHCARE SERVICE LAYER
SERVICE-MANAGEMENT-LAYER
USER LAYER
IOMT CLOUD TECHNOLOGIES
Cloud Computing
Big Data
Artificial Intelligence (AI)
IOMT CLOUD APPLICATIONS
IOMT EDGE CLOUD
IOMT EDGE-CLOUD ARCHITECTURE
IOMT EDGE CLOUD TECHNOLOGIES
Edge Computing
Computational Offload
IOMT EDGE CLOUD APPLICATIONS
CONCLUSION & FUTURE WORK
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Survey of IoMT Interference Mitigation Techniques for Wireless Body Area Networks (WBANs)
Izaz Ahmad1, Muhammad Abul Hassan1,*, Inam Ullah Khan2 and Farhatullah3
INTRODUCTION
Difference Between WBAN vs. WSN Concerning IoMT
Wireless Sensor Network (WSN)
WBAN ARCHITECTURE
WBAN APPLICATIONS
Rehabilitation and Therapy
Wearable Health Monitoring System
Disaster Aid Network
TECHNOLOGIES
Bluetooth
Low Energy Bluetooth
ZigBee
IEEE 802.11
IEEE 802.15.4
IEEE 802.15.6
TECHNIQUES AND COMPARISON
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Artificial Intelligence-Based IoT Applications in Future Pandemics
Tarun Virmani1,*, Anjali Sharma2, Ashwani Sharma3, Girish Kumar3 and Meenu Bhati3
INTRODUCTION
IOT AND AI IN HEALTH CARE
IOT AND AI: APPLICATIONS
AI AND IOT-ENABLED REMOTE SCREENING
Patients and IoT
IoT for Doctors
IoT in Hospitals
Diagnosis
MONITORING AND CONTROL OF EPIDEMIC VIA ML-BASED IOT
Drug Discovery and Vaccine Research
Applicability of AI-Enabled System
FUTURE PANDEMIC PREDICTION
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Cyber Secure AIoT Applications in Future Pandemics
Maria Nawaz Chohan1,* and Sana Nawaz Chohan2
INTRODUCTION
LITERATURE STUDY
ARTIFICIAL INTERNET OF THINGS APPLICATIONS FOR HEALTHCARE
H-AIoT Based Hardware
H-AIoT Based Software
Communication/Routing Protocols
UAV’s/Drones in the Healthcare Industry
Wearable AI-IoT Sensors
AI-IoT-Based Monitoring System
Detection of Cyber-Attacks in IoMT
Machine Learning Techniques for COVID-19
Industry 5.0 for Smart Healthcare Systems
Industry 5.0 Related Challenges
Using Flying Vehicles in Health Industry
Future Challenges
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Machine Learning Solution for Orthopedics: A Comprehensive Review
Muhammad Imad1,*, Muhammad Abul Hassan1, Shah Hussain Bangash1 and Naimullah1
INTRODUCTION
LITERATURE REVIEW
METHODOLOGY
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
A Review of Machine Learning Approaches for Identification of Health-Related Diseases
Muhammad Yaseen Ayub1,*, Farman Ali Khan1, Syeda Zillay Nain Zukhraf2 and Muhammad Hamza Akhlaq3
INTRODUCTION
Supervised Learning
Unsupervised Learning
MOTIVATION
LITERATURE STUDY
Heart Diseases Detection
Lung Diseases Detection
Skin Disease Detection
Brain Diseases Detection
Liver Diseases Detection
ALGORITHMS EXPLOITED FOR VARIOUS DISEASES DETECTION
TOOLS AND LIBRARIES USED FOR DISEASE DETECTION
CONCLUSION AND FUTURE TRENDS
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Machine Learning in Detection of Disease: Solutions and Open Challenges
Tayyab Rehman1, Noshina Tariq1, Ahthasham Sajid2,* and Muhammad Hamza Akhlaq3
INTRODUCTION
MACHINE LEARNING APPROACHES
Supervised Learning (SL)
Unsupervised Learning
Reinforcement Learning (RL)
Data Mining (DM)
DETECTION OF DISEASE BY USING DIFFERENT MACHINE-LEARNING CLASSIFICATION
CHRONIC DISEASE: DETECTION OF HEART DISEASE
Naive Bayes (NB)
Decision Tree (DT)
K-Nearest Neighbor (K-NN)
Issues and Challenges
CHRONIC DISEASE: DETECTION OF DISEASE BREAST CANCER
CAD System
Deep Learning
Machine-Learning Techniques
Convolutional Neural Network Model (CNN)
Issues and Challenges
CHRONIC DISEASE: DETECTION OF DISEASE DIABETES
Logistic Regression (LR)
Random Forest Classifier (RFC)
Gradient Boosted Trees (GBT)
Weighted Ensemble Model (WEM)
Issues and Challenges
CHRONIC DISEASE: DETECTION OF LIVER DISEASE
Data Selection and Pre-Processing
Feature Selection
Classification Algorithm
Supervised Learning and Unsupervised Learning
Performance Metrics Analysis
Predicted Results
Issues and Challenges
SEASONAL DISEASE: DETECTION OF DENGUE DISEASE
Issues and Challenges
SEASONAL DISEASE: DETECTION OF COVID-19 DISEASE
Issues and Challenges
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Breakthrough in Management of Cardiovascular Diseases by Artificial Intelligence in Healthcare Settings
Lakshmi Narasimha Gunturu1,*, Girirajasekhar Dornadula2 and Raghavendra Naveen Nimbagal3
INTRODUCTION
MATERIALS AND METHODS
ALGORITHMS USED IN CARDIOVASCULAR DISEASES
K-Nearest Neighbour (KNN)
Artificial Neural Network (ANN)
Decision Tree (DT)
Logistic Regression (LR)
AdaBoost (AB)
Support Vector Machine (SVM)
RESULTS AND DISCUSSION
Impact of AI on Echocardiography (ECG)
Role of AI on Magnetic Resonance Imaging (MRI)
Use of AI on Cardiac Computed Tomography (CT)
Impact of AI on Electrocardiography
CHALLENGES
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Smart Cane: Obstacle Recognition for Visually Impaired People Based on Convolutional Neural Network
Adnan Hussain1, Bilal Ahmad1 and Muhammad Imad2,*
INTRODUCTION
LITERATURE STUDY
MATERIALS AND METHODS
Dataset Description
Methods
Ultrasonic Sensors
Visual Sensor
Buzzer Sensor
Jumper Wires
Breadboard
Bus Strip
Socket Strip
Power Bank
Earphone/Speaker
Traditional Cane
Smart/Modern Cane
Proposed Device Architecture
Deep Convolutional Neural Network
EXPERIMENTAL RESULTS ANALYSIS
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
A Survey on Brain-Computer Interface and Related Applications
Krishna Pai1,*, Rakhee Kallimani1, Sridhar Iyer1, B. Uma Maheswari2, Rajashri Khanai1 and Dattaprasad Torse2
INTRODUCTION
RELATED WORKS
APPLICATIONS OF BCI
ISSUES AND CHALLENGES, AND FUTURE DIRECTIONS
Neuro-Psycho-Physiological Issues
Technical Issues
Ethical Issues
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Data Augmentation with Image Fusion Techniques for Brain Tumor Classification using Deep Learning
Tarik Hajji1,*, Ibtissam Elhassani1, Tawfik Masrour1, Imane Tailouloute1 and Mouad Dourhmi1
INTRODUCTION
BACKGROUND
Deep Learning
Data Augmentation
Image Fusion
Related Work
METHODOLOGY
Dataset
Deep Learning Approach with Classical Data Augmentation
Data Pre-Processing for the Model
Generation of many Manipulated Images from a Directory
Design of the Model Architecture
Convolution Layer
Pooling Layer
Flatten Layer
Dense Layer
Learning and Same Parameters
Data Augmentation: A Comparative Study
Data Augmentation with Image Fusion
Auto-Encoder Architecture
RESULTS AND DISCUSSION
CNN Result without Data Augmentation
CNN Result with Data Augmentation Automatic Generator
CNN Result-Based DA using IF with BWT
CNN Result-Based DA using IF with Auto-Encoder Proposed Approach
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Convergence Towards Blockchain-Based Patient Health Record and Sharing System: Emerging Issues and Challenges
Mahendra Kumar Shrivas1,*, Ashok Bhansali1, Hoshang Kolivand2 and Kamal Kant Hiran3
INTRODUCTION
METHODOLOGY
THE HEALTHCARE DATA MANAGEMENT SYSTEM (HDMS) OR HEALTHCARE INFORMATION SYSTEM (HIS)
Evolution of the Health Data Management System (HDMS)/ Health Information System (HIS)
Current Status, Issues, and Challenges
Huge Data Volume and Velocity and Paper-Based Record Keeping
Interoperability and Data Sharing
Data Governance, Manipulation, Privacy and Security Threats
BLOCKCHAIN FUNDAMENTALS, CONCEPTS, AND FEATURES
Blockchain Categorization
Evolution of Blockchain Technology
Transaction in Blockchain Network
HEALTHCARE AND BLOCKCHAIN
Blockchain-Based Systems Models for the HDMS/HIS
How Does Blockchain Address Security, Consensus, and Data Manipulation Issues?
How Does Blockchain Address Privacy Issues?
Preventing PHR, EMR Manipulation, and Sharing Records Securely using Blockchain
ISSUES, CHALLENGES, AND RECOMMENDATIONS
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Subject Index
