Artificial Intelligence in Telemedicine: Processing of Biosignals and Medical images

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Editors
Contributors
Part 1 Biosignal and Medical Image Processing for Disease Diagnosis
Chapter 1 Lightweight One-Dimensional CNN for Cardiac Arrhythmia Classification
1.1 Introduction
1.2 Materials and Methods
1.2.1 ECG Data Set
1.2.2 Synthetic Data Generation
1.3 Proposed 1D Convolutional Neural Network
1.4 Results and Discussion
1.5 Conclusion
References
Chapter 2 An Automatic System for Pertussis Diagnosis with Temperature Monitoring Using CNN
2.1 Introduction
2.2 Related Works
2.3 Database Description for Pertussis Detection
2.4 Detection of Pertussis Using Convolution Neural Network
2.5 Temperature Monitoring
2.6 Results and Discussion
2.7 Conclusion
References
Chapter 3 DocPresRec: Doctor's Handwritten Prescription Recognition Using Deep Learning Algorithm
3.1 Introduction
3.2 Related Works
3.3 Proposed Framework
3.3.1 Backbone Network
3.3.2 Region Proposal Network
3.3.3 Fast R-CNN
3.3.4 Mask Branch
3.3.5 Character and Text Instance Segmentation
3.3.6 Spatial Attentional Module
3.3.7 Position Embedding
3.3.8 Spatial Attention with RNNs
3.3.9 Decoding
3.4 Experimental Analysis
3.4.1 Dataset
3.4.2 Implementation Details
3.5 Results and Discussion
3.6 Conclusion
References
Chapter 4 An Efficient Deep Learning Approach for Brain Stroke Detection: Application of Telemedicine
4.1 Introduction
4.2 Materials and Methods
4.2.1 Types of Strokes
4.2.2 Proposed Hybrid Method for Stroke Detection
4.2.3 Initialisation and Hyper-Parameter Setting
4.2.4 Image Processing and Telemedicine
4.2.5 Dataset Description
4.2.6 Performance Parameters
4.3 Results and Discussion
4.3.1 Experimental Setup
4.3.2 Training and Validation
4.3.3 Testing Phase
4.3.4 Performance of SPP-VGG-NiN
4.3.5 Performance Comparison
4.4 Conclusion
References
Chapter 5 An Automated Detection of Notable ABCD Diagnostics of Melanoma in Dermoscopic Images
5.1 Introduction
5.2 Literature Survey
5.2.1 Limitations of Existing Methods
5.2.2 Motivation and Contribution
5.3 Proposed Methodology
5.3.1 ABCD Features of Melanoma
5.3.2 Asymmetry
5.3.3 Borders
5.3.4 Color Variegation
5.3.5 Diameter
5.4 Results and Discussion
5.5 Conclusion and Future Work
References
Chapter 6 Comparative Study of CNN and LSTM-Based Hand Gesture Classification Using EMG Signals
6.1 Introduction: Background and Driving Forces
6.2 EMG-Based HGR Systems
6.2.1 Datasets
6.2.2 Generic System
6.2.3 Preprocessing
6.2.4 Segmentation
6.2.5 Feature Extraction
6.2.6 Classification
6.3 Related Works
6.4 Convolutional Neural Network
6.5 Long Short-Term Memory
6.6 Results and Discussion
6.7 Conclusion
References
Chapter 7 Improved Transfer Learning-Enabled Heuristic Technique for Pneumonia Detection on Graphical Processing Unit Platform
7.1 Introduction
7.2 Transfer Learning–Based Approach for Pneumonia Detection Using VGG16 Deep Learning Model
7.3 Convolutional Neural Networks
7.4 Results and Discussion
7.5 Evaluation Results for Classification Model
7.6 Conclusion
References
Chapter 8 Improved Adaptive Learning Framework for an Effective Removal of Noise in ECG Signal for Telemedicine System
8.1 Introduction
8.1.1 Background Study
8.2 An Improved Adaptive Learning Approach for ECG Signal Noise Removal
8.3 Wavelet Thresholding and Wavelet Decomposition
8.4 Yule-Walker Modeling
8.5 Hidden Markov Model
8.6 Experimental Analysis
8.7 Conclusion
References
Part 2 Role of Artificial Intelligence and IoT in Health Care
Chapter 9 Human Disease Prediction System – Application of AI Techniques in Chronic Diseases
9.1 Introduction: Overview
9.2 Related Works
9.3 Data Mining Techniques in Disease Prediction
9.3.1 Intelligent Heart Disease Prediction System
9.3.2 Medical Diagnosis Using Back Propagation Algorithm
9.3.3 Disease Prediction System
9.3.4 Smart Health Prediction System
9.3.5 Chronic Kidney Disease Prediction
9.3.6 Machine Learning Over Big Data for Prediction
9.4 Neural Network Models
9.4.1 Real-Time Heart Disease Prediction System
9.4.2 Recommendation System Using Machine Learning and IoT
9.4.3 Decision Tree Classification Model
9.4.4 Classifier System Using Machine Learning Algorithms
9.5 Deep Learning Methods in Human Disease Prediction
9.5.1 Diabetic Retinopathy
9.5.2 Disease Diagnosis Based on Tongue Color Image Analysis
9.5.3 Skin Disease Image Recognition
9.5.4 COVID Care
9.6 Usage of Micro- or Nanorobots in Healthcare
9.7 Summary
References
Chapter 10 Internet of Things in Mental Healthcare Worldwide – A Study
10.1 Introduction
10.2 Healthcare System Architecture
10.3 Can the IoT Help with Your Mental Health?
10.3.1 IoT for Self-Care
10.3.2 IoT for Clinicians
10.3.3 Data Security Concerns
10.3.4 Progressive Steps
10.4 Role of Smart Devices and AI to Monitor Mental Health
10.4.1 A New, AI-Driven Era
10.4.2 Role of AI and IoT Technology in the Treatment of Mental Health Issues
10.5 Modernizing Mental Healthcare
10.6 IoT Will Challenge the Improved Plea on Mental Health Facilities Post-pandemic
10.7 IoT and the Future of Mental Health
10.7.1 Smart Wearable
10.7.2 Mobile Applications and Algorithms
10.7.3 Artificial Intelligence
10.7.4 Chatbots
10.7.5 E-Therapy
10.7.6 Smart Security and GPS Trackers
10.8 Benefits of IoT in Healthcare
10.9 Challenges of IoT in Healthcare
10.9.1 Data Security and Privacy
10.9.2 Integration: Multiple Devices and Protocols
10.9.3 Data Overload and Accuracy
10.9.4 Cost
10.10 Conclusion
References
Chapter 11 Internet of Things: A Promise to Smart Healthcare
11.1 Introduction
11.2 Internet of Things
11.3 IoT in Healthcare
11.4 Role of IoT in Various Disease Diagnosis and Prediction
11.4.1 Role of IoT in Diagnosis of Neurological Disorders
11.4.2 Role of IoT in Cancer Diagnosis
11.4.3 Role of IoT in Diabetes Diagnosis
11.4.4 Role of IoT in COVID-19 Diagnosis
11.5 Conclusion
Acknowledgements
References
Chapter 12 A Brief Review on Wireless Capsule Endoscopy Image Compression
12.1 Introduction
12.2 Image Compression Clinical Perspective
12.3 Review of Compression Algorithms for Wireless Capsule Endoscopy Images
12.4 Near-Lossless Image Compression Algorithms
12.5 Low-Power and Low-Complexity Image Compression
12.6 Wireless Capsule Compression Using Intelligent Learning Models
12.7 Result and Discussion
12.8 Research Findings and Limitations
12.9 Conclusion
References
Chapter 13 A Comprehensive Review on Leukemia Diseases Based on Microscopic Blood Cell Images
13.1 Introduction
13.2 Preliminaries Study on Leukemia
13.3 Machine Learning and Deep Learning Approaches in Leukemia Detection
13.4 Systematic Review
13.5 Scrutinization Criterion of Publication on Leukemia
13.6 Performance Analysis
13.7 Overall Review of Research Findings
13.8 Defects with the Current System
13.9 Conclusion
References
Chapter 14 Effects of Numerical Mapping Techniques on Performance in Genomic Signal Processing
14.1 Introduction
14.2 Examination of DNA Numerical Mapping Techniques and Their Numerical Representations
14.2.1 Cartesian Coordinate Properties
14.2.2 Biochemical and Physicochemical Properties
14.2.3 Binary and Information Encoding
14.2.4 Primary Structure Properties
14.3 Conclusion
References
Chapter 15 Importance of 5G/6G in Telemedicine System During the Pandemic Situation
15.1 Introduction
15.2 Importance of Telemedicine
15.3 Realization of Telemedicine
15.4 Conclusion
References
Chapter 16 Applications of Artificial Intelligence Techniques in Healthcare Industry
16.1 Introduction
16.2 AI in Chronic Diseases
16.2.1 ML in Diabetes
16.2.2 ML in Cardiovascular Disease
16.2.3 Cardiovascular Risk Prediction
16.2.4 Credentials of Novel Cardiovascular Disease Phenotypes
16.2.5 Summary
16.3 AI in Algorithmic Medicine
16.3.1 Computer Vision
16.4 AI in Thyroidology
16.4.1 Ultrasound Image Classification
16.5 AI and Drug Discovery
16.5.1 High-Throughput Screening
16.5.2 Deep Learning–Based Virtual Screening
16.6 Case Study
16.6.1 Segmentation for Osteoporosis Detection From X-Ray and CT Images
16.6.2 Deep Learning Framework for Retinal Segmentation
References
Index