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Submit a ProposalMedical Imaging and Health Informatics
 | Edited by Tushar H Jaware, K Sarat Kumar, Svetlin Antonov, and Ravindra D Badgujar
Series: Next-Generation Computing and Communication Engineering Copyright: 2022 | Status: Published ISBN: 9781119819134 | Hardcover | 382 pages | 212 illustrations Price: $225 USD  |
One Line Description
Provides a comprehensive review of artificial intelligence (AI) in medical imaging as well as practical recommendations for the usage of machine learning (ML) and deep learning (DL) techniques for clinical applications.
Audience
The core audience comprises researchers and industry engineers, scientists, radiologists, healthcare professionals, data scientists who work in health informatics, computer vision and medical image analysis.
The core audience comprises researchers and industry engineers, scientists, radiologists, healthcare professionals, data scientists who work in health informatics, computer vision and medical image analysis.
Description
Medical imaging and health informatics is a subfield of science and engineering which applies informatics to medicine and includes the study of design, development, and application of computational innovations to improve healthcare. The health domain has a wide range of challenges that can be addressed using computational approaches; therefore, the use of AI and associated technologies is becoming more common in society and healthcare. Currently, deep learning algorithms are a promising option for automated disease detection with high accuracy. Clinical data analysis employing these deep learning algorithms allows physicians to detect diseases earlier and treat patients more efficiently. Since these technologies have the potential to transform many aspects of patient care, disease detection, disease progression and pharmaceutical organization, approaches such as deep learning algorithms, convolutional neural networks, and image processing techniques are explored in this book.
This book also delves into a wide range of image segmentation, classification, registration, computer- aided analysis applications, methodologies, algorithms, platforms, and tools; and gives a holistic approach to the application of AI in healthcare through case studies and innovative applications. It also shows how image processing, machine learning and deep learning techniques can be applied for medical diagnostics in several specific health scenarios such as COVID-19, lung cancer, cardiovascular diseases, breast cancer, liver tumor, bone fractures, etc. Also highlighted are the significant issues and concerns regarding the use of AI in healthcare together with other allied areas, such as the Internet of Things (IoT) and medical informatics, to construct a global multidisciplinary forum.
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Medical imaging and health informatics is a subfield of science and engineering which applies informatics to medicine and includes the study of design, development, and application of computational innovations to improve healthcare. The health domain has a wide range of challenges that can be addressed using computational approaches; therefore, the use of AI and associated technologies is becoming more common in society and healthcare. Currently, deep learning algorithms are a promising option for automated disease detection with high accuracy. Clinical data analysis employing these deep learning algorithms allows physicians to detect diseases earlier and treat patients more efficiently. Since these technologies have the potential to transform many aspects of patient care, disease detection, disease progression and pharmaceutical organization, approaches such as deep learning algorithms, convolutional neural networks, and image processing techniques are explored in this book.
This book also delves into a wide range of image segmentation, classification, registration, computer- aided analysis applications, methodologies, algorithms, platforms, and tools; and gives a holistic approach to the application of AI in healthcare through case studies and innovative applications. It also shows how image processing, machine learning and deep learning techniques can be applied for medical diagnostics in several specific health scenarios such as COVID-19, lung cancer, cardiovascular diseases, breast cancer, liver tumor, bone fractures, etc. Also highlighted are the significant issues and concerns regarding the use of AI in healthcare together with other allied areas, such as the Internet of Things (IoT) and medical informatics, to construct a global multidisciplinary forum.
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Author / Editor Details
Tushar H. Jaware, PhD, received his degree in Medical Image Processing and is now an assistant professor in the Department of Electronics and Telecommunication Engineering, R C Patel Institute of Technology, Shirpur, India. He has published more than 50 research articles in refereed journals and IEEE conferences, and has three international patents granted and two Indian patents published.
Tushar H. Jaware, PhD, received his degree in Medical Image Processing and is now an assistant professor in the Department of Electronics and Telecommunication Engineering, R C Patel Institute of Technology, Shirpur, India. He has published more than 50 research articles in refereed journals and IEEE conferences, and has three international patents granted and two Indian patents published.
K. Sarat Kumar, PhD, received his degree in Electronics Engineering and is now a professor in the Department of Electronics & Communication Engineering, K L University, Andhra Pradesh, India.
Ravindra D. Badgujar, PhD, received his degree in Electronics Engineering and is now an assistant professor in the Department of Electronics and Telecommunication Engineering, R C Patel Institute of Technology, Shirpur, India. He has published many research articles in refereed journals and IEEE conferences as well as one international patent granted and two Indian patents published.
Svetlin Antonov, PhD, received his degree in Telecommunications and is now a lecturer in the Faculty of Telecommunications, TU-Sofia, Bulgaria. He is the author of several books and more than 60 peer-reviewed articles.
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Table of Contents
Preface
1. Machine Learning Approach for Medical Diagnosis Based on Prediction Model
Hemant Kasturiwale, Rajesh Karhe and Sujata N. Kale
1.1 Introduction
1.1.1 Heart System and Major Cardiac Diseases
1.1.2 ECG for Heart Rate Variability Analysis
1.1.3 HRV for Cardiac Analysis
1.2 Machine Learning Approach and Prediction
1.3 Material and Experimentation
1.3.1 Data and HRV
1.3.1.1 HRV Data Analysis via ECG Data Acquisition System
1.3.2 Methodology and Techniques
1.3.2.1 Classifiers and Performance Evaluation
1.3.3 Proposed Model With Layer Representation
1.3.4 The Model Using Fixed Set of Features and Standard Dataset
1.3.4.1 Performance of Classifiers With Feature Selection
1.4 Performance Metrics and Evaluation of Classifiers
1.4.1 Cardiac Disease Prediction Through Flexi Intra Group Selection Model
1.4.2 HRV Model With Flexi Set of Features
1.4.3 Performance of the Proposed Modified With ISM-24
1.5 Discussion and Conclusion
1.5.1 Conclusion and Future Scope
References
2. Applications of Machine Learning Techniques in Disease Detection
M.S. Roobini, Sowmiya M., S. Jancy and L. Suji Helen
2.1 Introduction
2.1.1 Overview of Machine Learning Types
2.1.2 Motivation
2.1.3 Organization the Chapter
2.2 Types of Machine Learning Techniques
2.2.1 Supervised Learning
2.2.2 Classification Algorithm
2.2.3 Regression Analysis
2.2.4 Linear Regression
2.2.4.1 Applications of Linear Regression
2.2.5 KNN Algorithm
2.2.5.1 Working of KNN
2.2.5.2 Drawbacks of KNN Algorithm
2.2.6 Decision Tree Classification Algorithm
2.2.6.1 Attribute Selection Measures
2.2.6.2 Information Gain
2.2.6.3 Gain Ratio
2.2.7 Random Forest Algorithm
2.2.7.1 How the Random Forest Algorithm Works
2.2.7.2 Advantage of Using Random Forest
2.2.7.3 Disadvantage of Using the Random Forest
2.2.8 Naive Bayes Classifier Algorithm
2.2.8.1 For What Reason is it Called Naive Bayes?
2.2.8.2 Disservices of Naive Bayes Classifier
2.2.9 Logistic Regression
2.2.9.1 Logistic Regression for Machine Learning
2.2.10 Support Vector Machine
2.2.11 Unsupervised Learning
2.2.11.1 Clustering
2.2.11.2 PCA in Machine Learning
2.2.12 Semi-Supervised Learning
2.2.12.1 What is Semi-Supervised Clustering?
2.2.12.2 How Semi-Supervised Learning Functions?
2.2.13 Reinforcement Learning
2.2.13.1 ArtificialIntelligence
2.2.13.2 Deep Learning
2.2.13.3 Points of Interest of Machine Learning
2.2.13.4 Why Machine Learning is Popular
2.2.13.5 Test Utilizations of ML
2.3 Future Research Directions
2.3.1 Privacy
2.3.2 Accuracy
References
3. Dengue Incidence Rate Prediction Using Nonlinear Autoregressive Neural Network Time Series Model
S. Dhamodharavadhani and R. Rathipriya
3.1 Introduction
3.2 Related Literature Study
3.2.1 Limitations of Existing Works
3.2.2 Contributions of Proposed Methodology
3.3 Methods and Materials
3.3.1 NAR-NNTS
3.3.2 Fit/Train the Model
3.3.3 Training Algorithms
3.3.3.1 Levenberg-Marquardt (LM) Algorithm
3.3.3.2 Bayesian Regularization (BR) Algorithm
3.3.3.3 Scaled Conjugate Gradient (SCG) Algorithm
3.3.4 DIR Prediction
3.4 Result Discussions
3.4.1 Dataset Description
3.4.2 Evaluation Measure for NAR-NNTS Models
3.4.3 Analysis of Results
3.5 Conclusion and Future Work Acknowledgment
References
4. Early Detection of Breast Cancer Using Machine Learning
G. Lavanya and G. Thilagavathi
4.1 Introduction
4.1.1 Objective
4.1.2 Anatomy of Breast
4.1.3 Breast Imaging Modalities
4.2 Methodology
4.2.1 Database
4.2.2 Image Pre-Processing
4.3 Segmentation
4.4 Feature Extraction
4.5 Classification
4.5.1 Naive Bayes Neural Network Classifier
4.5.2 Radial Basis Function Neural Network
4.5.2.1 Input
4.5.2.2 Hidden Layer
4.5.2.3 Output Nodes
4.6 Performance Evaluation Methods
4.7 Output
4.7.1 Dataset
4.7.2 Pre-Processing
4.7.3 Segmentation
4.7.4 Geometric Feature Extraction
4.8 Results and Discussion
4.8.1 Database
4.9 Conclusion and Future Scope
References
5. Machine Learning Approach for Prediction of Lung Cancer
Hemant Kasturiwale, Swati Bhisikar and Sandhya Save
5.1 Introduction
5.1.1 Disorders in Lungs
5.1.2 Background
5.1.3 Material, Datasets, and Techniques
5.2 Feature Extraction and Lung Cancer Analysis
5.3 Methodology
5.3.1 Proposed Algorithm Steps
5.3.2 Classifiers in Concurrence With Datasets
5.4 Proposed System and Implementation
5.4.1 Interpretation via Artificial Intelligence
5.4.2 Training of Model
5.4.3 Implementation and Results
5.5 Conclusion
5.5.1 Future Scope
References
6. Segmentation of Liver Tumor Using ANN
Hema L. K. and R. Indumathi
6.1 Introduction
6.2 Liver Tumor
6.2.1 Overview of Liver Tumor
6.2.2 Classification
6.2.2.1 Benign
6.2.2.2 Malignant
6.3 Benefits of CT to Diagnose Liver Cancer
6.4 Literature Review
6.5 Interactive Liver Tumor Segmentation by Deep Learning
6.6 Existing System
6.7 Proposed System
6.7.1 Pre-Processing
6.7.2 Segmentation
6.7.3 Feature Extraction
6.7.4 GLCM
6.7.5 Backpropagation Network
6.8 Result and Discussion
6.8.1 Processed Images
6.8.2 Segmentation
6.9 Future Enhancements
6.10 Conclusion
References
7. DMSAN: Deep Multi-Scale Attention Network for Automatic Liver Segmentation From Abdomen CT Images
Devidas T. Kushnure and Sanjay N. Talbar
7.1 Introduction
7.2 Related Work
7.3 Methodology
7.3.1 Proposed Architecture
7.3.2 Multi-Scale Feature Characterization Using Res2Net Module
7.4 Experimental Analysis
7.4.1 Dataset Description
7.4.2 Pre-Processing Dataset
7.4.3 Training Strategy
7.4.4 Loss Function
7.4.5 Implementation Platform
7.4.6 Data Augmentation
7.4.7 Performance Metrics
7.5 Results
7.6 Result Comparison With Other Methods
7.7 Discussion
7.8 Conclusion
Acknowledgement
References
8. AI-Based Identification and Prediction of Cardiac Disorders
Rajesh Karhe, Hemant Kasturiwale and Sujata N. Kale
8.1 Introduction
8.1.1 Cardiac Electrophysiology and Electrocardiogram
8.1.2 Heart Arrhythmia
8.1.2.1 Types of Arrhythmias
8.1.3 ECG Database
8.1.3.1 Association for the Advancement of Medical Instrumentation (AAMI) Standard
8.1.4 An Overview of ECG Signal Analysis
8.2 Related Work
8.3 Classifiers and Methodology
8.3.1 Databases for Cardiac Arrhythmia Detection
8.3.2 MIT-BIH Normal Sinus Rhythm and Arrhythmia Database
8.3.3 Arrhythmia Detection and Classification
8.3.4 Methodology
8.3.4.1 Database Gathering and Pre-Processing
8.3.4.2 QRST Wave Detection
8.3.4.3 Features Extraction
8.3.4.4 Neural Network
8.3.4.5 Performance Evaluation
8.4 Result Analysis
8.4.1 Arrhythmia Detection and Classification
8.4.2 Dataset
8.4.3 Evaluations and Results
8.4.4 Evaluating the Performance of Various Neural Network Classifiers (Arrhythmia Detection)
8.5 Conclusions and Future Scope
8.5.1 Arrhythmia Detection and Classification
8.5.2 Future Scope
References
9. An Implementation of Image Processing Technique for Bone Fracture
Detection Including Classification
Rocky Upadhyay, Prakash Singh Tanwar and Sheshang Degadwala
9.1 Introduction
9.2 Existing Technology
9.2.1 Pre-Processing
9.2.2 Denoise Image
9.2.3 Histogram
9.3 Image Processing
9.3.1 Canny Edge
9.4 Overview of System and Steps
9.4.1 Workflow
9.4.2 Classifiers
9.4.2.1 Extra Tree Ensemble Method
9.4.2.2 SVM
9.4.2.3 Trained Algorithm
9.4.3 Feature Extraction
9.5 Results
9.5.1 Result Analysis
9.6 Conclusion
References
10. Improved Otsu Algorithm for Segmentation of Malaria Parasite Images
Mosam K. Sangole, Sanjay T. Gandhe and Dipak P. Patil
10.1 Introduction
10.2 Literature Review
10.3 Related Works
10.4 Proposed Algorithm
10.5 Experimental Results
10.6 Conclusion
References
11. A Reliable and Fully Automated Diagnosis of COVID-19 Based on Computed Tomography
Bramah Hazela, Saad Bin Khalid and Pallavi Asthana
11.1 Introduction
11.2 Background
11.3 Methodology
11.3.1 Models Used
11.3.2 Architecture of the Image Source Classification Model
11.3.3 Architecture of the CT Scan Classification Model
11.3.4 Architecture of the Ultrasound Image Classification Model
11.3.5 Architecture of the X-Ray Classification Model
11.3.6 Dataset
11.3.6.1 Training
11.4 Results
11.5 Conclusion
References
12. Multimodality Medical Images for Healthcare Disease Analysis
B. Rajalingam, R. Santhoshkumar, P. Santosh Kumar Patra, M. Narayanan, G. Govinda Rajulu and T. Poongothai
12.1 Introduction
12.1.1 Background
12.2 Brief Survey of Earlier Works
12.3 Medical Imaging Modalities
12.3.1 Computed Tomography (CT)
12.3.2 Magnetic Resonance Imaging (MRI)
12.3.3 Positron Emission Tomography (PET)
12.3.4 Single-Photon Emission Computed Tomography (SPECT)
12.4 Image Fusion
12.4.1 Different Levels of Image Fusion
12.4.1.1 Pixel Level Fusion
12.4.1.2 Feature Level Fusion
12.4.1.3 Decision Level Fusion
12.5 Clinical Relevance for Medical Image Fusion
12.5.1 Clinical Relevance for Neurocyticercosis (NCC)
12.5.2 Clinical Relevance for Neoplastic Disease
12.5.2.1 Clinical Relevance for Astrocytoma
12.5.2.2 Clinical Relevance for Anaplastic Astrocytoma
12.5.2.3 Clinical Relevance for Metastatic Bronchogenic Carcinoma
12.5.3 Clinical Relevance for Alzheimer’s Disease
12.6 Data Sets and Softwares Used
12.7 Generalized Image Fusion Scheme
12.7.1 Input Image Modalities
12.7.2 Image Registration
12.7.3 Fusion Process
12.7.4 Fusion Rule
12.7.5 Evaluation
12.7.5.1 Subjective Evaluation
12.7.5.2 Objective Evaluation
12.8 Medical Image Fusion Methods
12.8.1 Traditional Image Fusion Techniques
12.8.1.1 Spatial Domain Image Fusion Approach
12.8.1.2 Transform Domain Image Fusion Approach
12.8.1.3 Fuzzy Logic–Based Image Fusion Approach
12.8.1.4 Filtering Technique–Based Image Fusion Approach
12.8.2 Hybrid Image Fusion Techniques
12.8.1.5 Neural Network–Based Image Fusion Approach
12.8.2.1 Transforms with Fuzzy Logic–Based Medical Image Fusion
12.8.2.2 Transforms With Guided Image Filtering–Based Medical Image Fusion 12.8.2.3 Transforms With Neural Network–Based Image Fusion
12.9 Conclusions
12.9.1 Future Work
References
13. Health Detection System for COVID-19 Patients Using IoT
Dipak P. Patil, Kishor Badane, Amit Kumar Mishra and Vishal A. Wankhede
13.1 Introduction
13.1.1 Overview
13.1.2 Preventions
13.1.3 Symptoms
13.1.4 Present Situation
13.2 Related Works
13.3 System Design
13.3.1 Hardware Implementation
13.3.1.1 NodeMCU
13.3.1.2 DHT 11 Sensor
13.3.1.3 MAX30100 Oxygen Sensor
13.3.1.4 ThingSpeakServer
13.3.1.5 Arduino IDE
13.4 Proposed System for Detection of Corona Patients
13.4.1 Introduction
13.4.2 Arduino IDE
13.4.3 Hardware Implementation
13.5 Results and Performance Analysis
13.5.1 Hardware Implementation
13.5.1.1 Implementation of NodeMCU With Temperature Sensor
13.5.2 Software Implementation
13.5.2.1 Simulation of Temperature Sensor With Arduino on Proteus Software
13.5.2.2 Interfacing of LCD With Arduino
13.6 Conclusion
References
14. Intelligent Systems in Healthcare
Rajiv Dey and Pankaj Sahu
14.1 Introduction
14.2 Brain Computer Interface
14.2.1 Types of Signals Used in BCI
14.2.2 Components of BCI
14.2.3 Applications of BCI in Health Monitoring
14.3 Robotic Systems
14.3.1 Advantages of Surgical Robots
14.3.2 Centralization of the Important Information to the Surgeon
14.3.3 Remote-Surgery, Software Development, and High Speed Connectivity Such as 5G
14.4 Voice Recognition Systems
14.5 Remote Health Monitoring Systems
14.5.1 Tele-Medicine Health Concerns
14.6 Internet of Things–Based Intelligent Systems
14.6.1 Ubiquitous Computing Technologies in Healthcare
14.6.2 Patient Bio-Signals and Acquisition Methods
14.6.3 Communication Technologies Used in Healthcare Application
14.6.4 Communication Technologies Based on Location/Position
14.7 Intelligent Electronic Healthcare Systems
14.7.1 The Background of Electronic Healthcare Systems
14.7.2 Intelligent Agents in Electronic Healthcare System
14.7.3 Patient Data Classification Techniques
14.8 Conclusion
References
15. Design of Antennas for Microwave Imaging Techniques
Dnyaneshwar D. Ahire, Gajanan K. Kharate and Ammar Muthana
15.1 Introduction
15.1.1 Overview
15.2 Literature
15.2.1 Microstrip Patch Antenna
15.2.2 Early Detection of Breast Cancer and Microstrip Patch Antenna for Biomedical Application
15.2.3 UWB for Microwave Imaging
15.3 Design and Development of Wideband Antenna
15.3.1 Overview
15.3.2 Design of Rectangular Microstrip Patch Antenna
15.3.3 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna
15.3.4 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground
15.3.5 Key Shape Monopole Rectangular Microstrip Patch Antenna With Rounded Corner in Partial Ground
15.4 Results and Inferences
15.4.1 Overview
15.4.2 Rectangular Microstrip Patch Antenna
15.4.2.1 Reflection and VSWR Bandwidth
15.4.2.2 Surface Current Distribution
15.4.3 Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground
15.4.3.1 Reflection and VSWR Bandwidth
15.4.3.2 Surface Current Distribution
15.4.3.3 Inference
15.4.4 Key Shape Monopole Rectangular Microstrip Patch Antenna with Rounded Corner in Partial Ground
15.4.4.1 Reflection and VSWR Bandwidth
15.4.4.2 Surface Current Distribution
15.4.4.3 Results of the Fabricated Antenna
15.4.4.4 Inference
15.5 Conclusion
References
16. COVID-19: A Global Crisis
Savita Mandan and Durgeshwari Kalal
16.1 Introduction
16.1.1 Structure
16.1.2 Classification of Corona Virus
16.1.3 Types of Human Coronavirus
16.1.4 Genome Organization of Corona Virus
16.1.5 Coronavirus Replication
16.1.6 Host Defenses
16.2 Clinical Manifestation and Pathogenesis
16.2.1 Symptoms
16.2.2 Epidemiology
16.3 Diagnosis and Control
16.3.1 Molecular Test
16.3.2 Serology
16.3.3 Concerning Lab Assessments
16.3.4 Significantly Improved D-Dimer
16.3.5 Imaging
16.3.6 HRCT
16.3.7 Lung Ultrasound
16.4 Control Measures
16.4.1 Prevention and Patient Education
16.5 Immunization
16.5.1 Medications
16.6 Conclusion
References
17. Smart Healthcare for Pregnant Women in Rural Areas
D. Shanthi
17.1 Introduction
17.2 National/International Surveys Reviews
17.2.1 National Family Health Survey Review-11
17.2.2 National Family Health Survey Review-2.2
17.2.3 National Family Health Survey Reviews-3
17.3 Architecture
17.4 Anganwadi’s Collaborative Work
17.5 Schemes Offered by Central/State Governments
17.5.1 AAH (Anna Amrutha Hastham)
17.5.2 Programme Arogya Laxmi
17.5.3 Balamrutham-Kids’ Weaning Food from 7 Months to 3 Years
17.5.4 Nutri TASC (Tracking of Group Responsibility for Services)
17.5.5 Akshyapatra Foundation (ISKCON)
17.5.6 Mahila Sishu Chaitanyam
17.5.7 Community Management of Acute Malnutrition
17.5.8 Child Health Nutrition Committee
17.5.9 Bharat Ratna APJ Abdul Kalam Amrut Yojna
17.6 Smart Healthcare System
17.7 Data Collection
17.8 Hardware and Software Features of HCS
17.9 Implementation
17.9.1 Modules
17.9.2 Modules Description
17.9.2.1 Data Preprocessing
17.9.2.2 Component Features Extraction
17.9.2.3 User Sentimental Measurement
17.9.2.4 Sentiment Evaluation
17.10 Results and Analysis
17.11 Conclusion
References
18. Computer-Aided Interpretation of ECG Signal—A Challenge
Shalini Sahay and A.K. Wadhwani
18.1 Introduction
18.1.1 Electrical Activity of the Heart
18.2 The Cardiovascular System
18.3 Electrocardiogram Leads
18.4 Artifacts/Noises Affecting the ECG
18.4.1 Baseline Wander
18.4.2 Power Line Interference
18.4.3 Motion Artifacts
18.4.4 Muscle Noise
18.4.5 Instrumentation Noise
18.4.6 Other Interferences
18.5 The ECG Waveform
18.5.1 Normal Sinus Rhythm
18.6 Cardiac Arrhythmias
18.6.1 Sinus Bradycardia
18.6.2 Sinus Tachycardia
18.6.3 Atrial Flutter
18.6.4 Atrial Fibrillation
18.6.5 Ventricular Tachycardia
18.6.6 AV Block 2 First Degree
18.6.7 Asystole
18.7 Electrocardiogram Databases
18.8 Computer-Aided Interpretation (CAD)
18.9 Computational Techniques
18.10 Conclusion
References
Index
Back to Top
Preface
1. Machine Learning Approach for Medical Diagnosis Based on Prediction Model
Hemant Kasturiwale, Rajesh Karhe and Sujata N. Kale
1.1 Introduction
1.1.1 Heart System and Major Cardiac Diseases
1.1.2 ECG for Heart Rate Variability Analysis
1.1.3 HRV for Cardiac Analysis
1.2 Machine Learning Approach and Prediction
1.3 Material and Experimentation
1.3.1 Data and HRV
1.3.1.1 HRV Data Analysis via ECG Data Acquisition System
1.3.2 Methodology and Techniques
1.3.2.1 Classifiers and Performance Evaluation
1.3.3 Proposed Model With Layer Representation
1.3.4 The Model Using Fixed Set of Features and Standard Dataset
1.3.4.1 Performance of Classifiers With Feature Selection
1.4 Performance Metrics and Evaluation of Classifiers
1.4.1 Cardiac Disease Prediction Through Flexi Intra Group Selection Model
1.4.2 HRV Model With Flexi Set of Features
1.4.3 Performance of the Proposed Modified With ISM-24
1.5 Discussion and Conclusion
1.5.1 Conclusion and Future Scope
References
2. Applications of Machine Learning Techniques in Disease Detection
M.S. Roobini, Sowmiya M., S. Jancy and L. Suji Helen
2.1 Introduction
2.1.1 Overview of Machine Learning Types
2.1.2 Motivation
2.1.3 Organization the Chapter
2.2 Types of Machine Learning Techniques
2.2.1 Supervised Learning
2.2.2 Classification Algorithm
2.2.3 Regression Analysis
2.2.4 Linear Regression
2.2.4.1 Applications of Linear Regression
2.2.5 KNN Algorithm
2.2.5.1 Working of KNN
2.2.5.2 Drawbacks of KNN Algorithm
2.2.6 Decision Tree Classification Algorithm
2.2.6.1 Attribute Selection Measures
2.2.6.2 Information Gain
2.2.6.3 Gain Ratio
2.2.7 Random Forest Algorithm
2.2.7.1 How the Random Forest Algorithm Works
2.2.7.2 Advantage of Using Random Forest
2.2.7.3 Disadvantage of Using the Random Forest
2.2.8 Naive Bayes Classifier Algorithm
2.2.8.1 For What Reason is it Called Naive Bayes?
2.2.8.2 Disservices of Naive Bayes Classifier
2.2.9 Logistic Regression
2.2.9.1 Logistic Regression for Machine Learning
2.2.10 Support Vector Machine
2.2.11 Unsupervised Learning
2.2.11.1 Clustering
2.2.11.2 PCA in Machine Learning
2.2.12 Semi-Supervised Learning
2.2.12.1 What is Semi-Supervised Clustering?
2.2.12.2 How Semi-Supervised Learning Functions?
2.2.13 Reinforcement Learning
2.2.13.1 ArtificialIntelligence
2.2.13.2 Deep Learning
2.2.13.3 Points of Interest of Machine Learning
2.2.13.4 Why Machine Learning is Popular
2.2.13.5 Test Utilizations of ML
2.3 Future Research Directions
2.3.1 Privacy
2.3.2 Accuracy
References
3. Dengue Incidence Rate Prediction Using Nonlinear Autoregressive Neural Network Time Series Model
S. Dhamodharavadhani and R. Rathipriya
3.1 Introduction
3.2 Related Literature Study
3.2.1 Limitations of Existing Works
3.2.2 Contributions of Proposed Methodology
3.3 Methods and Materials
3.3.1 NAR-NNTS
3.3.2 Fit/Train the Model
3.3.3 Training Algorithms
3.3.3.1 Levenberg-Marquardt (LM) Algorithm
3.3.3.2 Bayesian Regularization (BR) Algorithm
3.3.3.3 Scaled Conjugate Gradient (SCG) Algorithm
3.3.4 DIR Prediction
3.4 Result Discussions
3.4.1 Dataset Description
3.4.2 Evaluation Measure for NAR-NNTS Models
3.4.3 Analysis of Results
3.5 Conclusion and Future Work Acknowledgment
References
4. Early Detection of Breast Cancer Using Machine Learning
G. Lavanya and G. Thilagavathi
4.1 Introduction
4.1.1 Objective
4.1.2 Anatomy of Breast
4.1.3 Breast Imaging Modalities
4.2 Methodology
4.2.1 Database
4.2.2 Image Pre-Processing
4.3 Segmentation
4.4 Feature Extraction
4.5 Classification
4.5.1 Naive Bayes Neural Network Classifier
4.5.2 Radial Basis Function Neural Network
4.5.2.1 Input
4.5.2.2 Hidden Layer
4.5.2.3 Output Nodes
4.6 Performance Evaluation Methods
4.7 Output
4.7.1 Dataset
4.7.2 Pre-Processing
4.7.3 Segmentation
4.7.4 Geometric Feature Extraction
4.8 Results and Discussion
4.8.1 Database
4.9 Conclusion and Future Scope
References
5. Machine Learning Approach for Prediction of Lung Cancer
Hemant Kasturiwale, Swati Bhisikar and Sandhya Save
5.1 Introduction
5.1.1 Disorders in Lungs
5.1.2 Background
5.1.3 Material, Datasets, and Techniques
5.2 Feature Extraction and Lung Cancer Analysis
5.3 Methodology
5.3.1 Proposed Algorithm Steps
5.3.2 Classifiers in Concurrence With Datasets
5.4 Proposed System and Implementation
5.4.1 Interpretation via Artificial Intelligence
5.4.2 Training of Model
5.4.3 Implementation and Results
5.5 Conclusion
5.5.1 Future Scope
References
6. Segmentation of Liver Tumor Using ANN
Hema L. K. and R. Indumathi
6.1 Introduction
6.2 Liver Tumor
6.2.1 Overview of Liver Tumor
6.2.2 Classification
6.2.2.1 Benign
6.2.2.2 Malignant
6.3 Benefits of CT to Diagnose Liver Cancer
6.4 Literature Review
6.5 Interactive Liver Tumor Segmentation by Deep Learning
6.6 Existing System
6.7 Proposed System
6.7.1 Pre-Processing
6.7.2 Segmentation
6.7.3 Feature Extraction
6.7.4 GLCM
6.7.5 Backpropagation Network
6.8 Result and Discussion
6.8.1 Processed Images
6.8.2 Segmentation
6.9 Future Enhancements
6.10 Conclusion
References
7. DMSAN: Deep Multi-Scale Attention Network for Automatic Liver Segmentation From Abdomen CT Images
Devidas T. Kushnure and Sanjay N. Talbar
7.1 Introduction
7.2 Related Work
7.3 Methodology
7.3.1 Proposed Architecture
7.3.2 Multi-Scale Feature Characterization Using Res2Net Module
7.4 Experimental Analysis
7.4.1 Dataset Description
7.4.2 Pre-Processing Dataset
7.4.3 Training Strategy
7.4.4 Loss Function
7.4.5 Implementation Platform
7.4.6 Data Augmentation
7.4.7 Performance Metrics
7.5 Results
7.6 Result Comparison With Other Methods
7.7 Discussion
7.8 Conclusion
Acknowledgement
References
8. AI-Based Identification and Prediction of Cardiac Disorders
Rajesh Karhe, Hemant Kasturiwale and Sujata N. Kale
8.1 Introduction
8.1.1 Cardiac Electrophysiology and Electrocardiogram
8.1.2 Heart Arrhythmia
8.1.2.1 Types of Arrhythmias
8.1.3 ECG Database
8.1.3.1 Association for the Advancement of Medical Instrumentation (AAMI) Standard
8.1.4 An Overview of ECG Signal Analysis
8.2 Related Work
8.3 Classifiers and Methodology
8.3.1 Databases for Cardiac Arrhythmia Detection
8.3.2 MIT-BIH Normal Sinus Rhythm and Arrhythmia Database
8.3.3 Arrhythmia Detection and Classification
8.3.4 Methodology
8.3.4.1 Database Gathering and Pre-Processing
8.3.4.2 QRST Wave Detection
8.3.4.3 Features Extraction
8.3.4.4 Neural Network
8.3.4.5 Performance Evaluation
8.4 Result Analysis
8.4.1 Arrhythmia Detection and Classification
8.4.2 Dataset
8.4.3 Evaluations and Results
8.4.4 Evaluating the Performance of Various Neural Network Classifiers (Arrhythmia Detection)
8.5 Conclusions and Future Scope
8.5.1 Arrhythmia Detection and Classification
8.5.2 Future Scope
References
9. An Implementation of Image Processing Technique for Bone Fracture
Detection Including Classification
Rocky Upadhyay, Prakash Singh Tanwar and Sheshang Degadwala
9.1 Introduction
9.2 Existing Technology
9.2.1 Pre-Processing
9.2.2 Denoise Image
9.2.3 Histogram
9.3 Image Processing
9.3.1 Canny Edge
9.4 Overview of System and Steps
9.4.1 Workflow
9.4.2 Classifiers
9.4.2.1 Extra Tree Ensemble Method
9.4.2.2 SVM
9.4.2.3 Trained Algorithm
9.4.3 Feature Extraction
9.5 Results
9.5.1 Result Analysis
9.6 Conclusion
References
10. Improved Otsu Algorithm for Segmentation of Malaria Parasite Images
Mosam K. Sangole, Sanjay T. Gandhe and Dipak P. Patil
10.1 Introduction
10.2 Literature Review
10.3 Related Works
10.4 Proposed Algorithm
10.5 Experimental Results
10.6 Conclusion
References
11. A Reliable and Fully Automated Diagnosis of COVID-19 Based on Computed Tomography
Bramah Hazela, Saad Bin Khalid and Pallavi Asthana
11.1 Introduction
11.2 Background
11.3 Methodology
11.3.1 Models Used
11.3.2 Architecture of the Image Source Classification Model
11.3.3 Architecture of the CT Scan Classification Model
11.3.4 Architecture of the Ultrasound Image Classification Model
11.3.5 Architecture of the X-Ray Classification Model
11.3.6 Dataset
11.3.6.1 Training
11.4 Results
11.5 Conclusion
References
12. Multimodality Medical Images for Healthcare Disease Analysis
B. Rajalingam, R. Santhoshkumar, P. Santosh Kumar Patra, M. Narayanan, G. Govinda Rajulu and T. Poongothai
12.1 Introduction
12.1.1 Background
12.2 Brief Survey of Earlier Works
12.3 Medical Imaging Modalities
12.3.1 Computed Tomography (CT)
12.3.2 Magnetic Resonance Imaging (MRI)
12.3.3 Positron Emission Tomography (PET)
12.3.4 Single-Photon Emission Computed Tomography (SPECT)
12.4 Image Fusion
12.4.1 Different Levels of Image Fusion
12.4.1.1 Pixel Level Fusion
12.4.1.2 Feature Level Fusion
12.4.1.3 Decision Level Fusion
12.5 Clinical Relevance for Medical Image Fusion
12.5.1 Clinical Relevance for Neurocyticercosis (NCC)
12.5.2 Clinical Relevance for Neoplastic Disease
12.5.2.1 Clinical Relevance for Astrocytoma
12.5.2.2 Clinical Relevance for Anaplastic Astrocytoma
12.5.2.3 Clinical Relevance for Metastatic Bronchogenic Carcinoma
12.5.3 Clinical Relevance for Alzheimer’s Disease
12.6 Data Sets and Softwares Used
12.7 Generalized Image Fusion Scheme
12.7.1 Input Image Modalities
12.7.2 Image Registration
12.7.3 Fusion Process
12.7.4 Fusion Rule
12.7.5 Evaluation
12.7.5.1 Subjective Evaluation
12.7.5.2 Objective Evaluation
12.8 Medical Image Fusion Methods
12.8.1 Traditional Image Fusion Techniques
12.8.1.1 Spatial Domain Image Fusion Approach
12.8.1.2 Transform Domain Image Fusion Approach
12.8.1.3 Fuzzy Logic–Based Image Fusion Approach
12.8.1.4 Filtering Technique–Based Image Fusion Approach
12.8.2 Hybrid Image Fusion Techniques
12.8.1.5 Neural Network–Based Image Fusion Approach
12.8.2.1 Transforms with Fuzzy Logic–Based Medical Image Fusion
12.8.2.2 Transforms With Guided Image Filtering–Based Medical Image Fusion 12.8.2.3 Transforms With Neural Network–Based Image Fusion
12.9 Conclusions
12.9.1 Future Work
References
13. Health Detection System for COVID-19 Patients Using IoT
Dipak P. Patil, Kishor Badane, Amit Kumar Mishra and Vishal A. Wankhede
13.1 Introduction
13.1.1 Overview
13.1.2 Preventions
13.1.3 Symptoms
13.1.4 Present Situation
13.2 Related Works
13.3 System Design
13.3.1 Hardware Implementation
13.3.1.1 NodeMCU
13.3.1.2 DHT 11 Sensor
13.3.1.3 MAX30100 Oxygen Sensor
13.3.1.4 ThingSpeakServer
13.3.1.5 Arduino IDE
13.4 Proposed System for Detection of Corona Patients
13.4.1 Introduction
13.4.2 Arduino IDE
13.4.3 Hardware Implementation
13.5 Results and Performance Analysis
13.5.1 Hardware Implementation
13.5.1.1 Implementation of NodeMCU With Temperature Sensor
13.5.2 Software Implementation
13.5.2.1 Simulation of Temperature Sensor With Arduino on Proteus Software
13.5.2.2 Interfacing of LCD With Arduino
13.6 Conclusion
References
14. Intelligent Systems in Healthcare
Rajiv Dey and Pankaj Sahu
14.1 Introduction
14.2 Brain Computer Interface
14.2.1 Types of Signals Used in BCI
14.2.2 Components of BCI
14.2.3 Applications of BCI in Health Monitoring
14.3 Robotic Systems
14.3.1 Advantages of Surgical Robots
14.3.2 Centralization of the Important Information to the Surgeon
14.3.3 Remote-Surgery, Software Development, and High Speed Connectivity Such as 5G
14.4 Voice Recognition Systems
14.5 Remote Health Monitoring Systems
14.5.1 Tele-Medicine Health Concerns
14.6 Internet of Things–Based Intelligent Systems
14.6.1 Ubiquitous Computing Technologies in Healthcare
14.6.2 Patient Bio-Signals and Acquisition Methods
14.6.3 Communication Technologies Used in Healthcare Application
14.6.4 Communication Technologies Based on Location/Position
14.7 Intelligent Electronic Healthcare Systems
14.7.1 The Background of Electronic Healthcare Systems
14.7.2 Intelligent Agents in Electronic Healthcare System
14.7.3 Patient Data Classification Techniques
14.8 Conclusion
References
15. Design of Antennas for Microwave Imaging Techniques
Dnyaneshwar D. Ahire, Gajanan K. Kharate and Ammar Muthana
15.1 Introduction
15.1.1 Overview
15.2 Literature
15.2.1 Microstrip Patch Antenna
15.2.2 Early Detection of Breast Cancer and Microstrip Patch Antenna for Biomedical Application
15.2.3 UWB for Microwave Imaging
15.3 Design and Development of Wideband Antenna
15.3.1 Overview
15.3.2 Design of Rectangular Microstrip Patch Antenna
15.3.3 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna
15.3.4 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground
15.3.5 Key Shape Monopole Rectangular Microstrip Patch Antenna With Rounded Corner in Partial Ground
15.4 Results and Inferences
15.4.1 Overview
15.4.2 Rectangular Microstrip Patch Antenna
15.4.2.1 Reflection and VSWR Bandwidth
15.4.2.2 Surface Current Distribution
15.4.3 Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground
15.4.3.1 Reflection and VSWR Bandwidth
15.4.3.2 Surface Current Distribution
15.4.3.3 Inference
15.4.4 Key Shape Monopole Rectangular Microstrip Patch Antenna with Rounded Corner in Partial Ground
15.4.4.1 Reflection and VSWR Bandwidth
15.4.4.2 Surface Current Distribution
15.4.4.3 Results of the Fabricated Antenna
15.4.4.4 Inference
15.5 Conclusion
References
16. COVID-19: A Global Crisis
Savita Mandan and Durgeshwari Kalal
16.1 Introduction
16.1.1 Structure
16.1.2 Classification of Corona Virus
16.1.3 Types of Human Coronavirus
16.1.4 Genome Organization of Corona Virus
16.1.5 Coronavirus Replication
16.1.6 Host Defenses
16.2 Clinical Manifestation and Pathogenesis
16.2.1 Symptoms
16.2.2 Epidemiology
16.3 Diagnosis and Control
16.3.1 Molecular Test
16.3.2 Serology
16.3.3 Concerning Lab Assessments
16.3.4 Significantly Improved D-Dimer
16.3.5 Imaging
16.3.6 HRCT
16.3.7 Lung Ultrasound
16.4 Control Measures
16.4.1 Prevention and Patient Education
16.5 Immunization
16.5.1 Medications
16.6 Conclusion
References
17. Smart Healthcare for Pregnant Women in Rural Areas
D. Shanthi
17.1 Introduction
17.2 National/International Surveys Reviews
17.2.1 National Family Health Survey Review-11
17.2.2 National Family Health Survey Review-2.2
17.2.3 National Family Health Survey Reviews-3
17.3 Architecture
17.4 Anganwadi’s Collaborative Work
17.5 Schemes Offered by Central/State Governments
17.5.1 AAH (Anna Amrutha Hastham)
17.5.2 Programme Arogya Laxmi
17.5.3 Balamrutham-Kids’ Weaning Food from 7 Months to 3 Years
17.5.4 Nutri TASC (Tracking of Group Responsibility for Services)
17.5.5 Akshyapatra Foundation (ISKCON)
17.5.6 Mahila Sishu Chaitanyam
17.5.7 Community Management of Acute Malnutrition
17.5.8 Child Health Nutrition Committee
17.5.9 Bharat Ratna APJ Abdul Kalam Amrut Yojna
17.6 Smart Healthcare System
17.7 Data Collection
17.8 Hardware and Software Features of HCS
17.9 Implementation
17.9.1 Modules
17.9.2 Modules Description
17.9.2.1 Data Preprocessing
17.9.2.2 Component Features Extraction
17.9.2.3 User Sentimental Measurement
17.9.2.4 Sentiment Evaluation
17.10 Results and Analysis
17.11 Conclusion
References
18. Computer-Aided Interpretation of ECG Signal—A Challenge
Shalini Sahay and A.K. Wadhwani
18.1 Introduction
18.1.1 Electrical Activity of the Heart
18.2 The Cardiovascular System
18.3 Electrocardiogram Leads
18.4 Artifacts/Noises Affecting the ECG
18.4.1 Baseline Wander
18.4.2 Power Line Interference
18.4.3 Motion Artifacts
18.4.4 Muscle Noise
18.4.5 Instrumentation Noise
18.4.6 Other Interferences
18.5 The ECG Waveform
18.5.1 Normal Sinus Rhythm
18.6 Cardiac Arrhythmias
18.6.1 Sinus Bradycardia
18.6.2 Sinus Tachycardia
18.6.3 Atrial Flutter
18.6.4 Atrial Fibrillation
18.6.5 Ventricular Tachycardia
18.6.6 AV Block 2 First Degree
18.6.7 Asystole
18.7 Electrocardiogram Databases
18.8 Computer-Aided Interpretation (CAD)
18.9 Computational Techniques
18.10 Conclusion
References
Index
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