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Medical Imaging and Health Informatics

ISBN: 9781119819134
Εκδόσεις:
Διαστάσεις 26 × 19 cm
Μορφή

Έντυπο

Εκδόσεις

Ημ. Έκδοσης

2022/08

Σελίδες

384

Έκδοση

1η έκδοση

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, ,

Κύριος Συγγραφέας

235,00€(Περιλαμβάνεται ΦΠΑ 6%)

Διαθεσιμότητα: 23-28 ημέρες

Περιγραφή

MEDICAL IMAGING AND HEALTH INFORMATICS

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.

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.

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.

About the Author

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.

Περιεχόμενα

Preface xvii

1 Machine Learning Approach for Medical Diagnosis Based on Prediction Model 1
Hemant Kasturiwale, Rajesh Karhe and Sujata N. Kale

1.1 Introduction 2

1.1.1 Heart System and Major Cardiac Diseases 2

1.1.2 ECG for Heart Rate Variability Analysis 2

1.1.3 HRV for Cardiac Analysis 3

1.2 Machine Learning Approach and Prediction 3

1.3 Material and Experimentation 4

1.3.1 Data and HRV 4

1.3.1.1 HRV Data Analysis via ECG Data Acquisition System 5

1.3.2 Methodology and Techniques 6

1.3.2.1 Classifiers and Performance Evaluation 7

1.3.3 Proposed Model With Layer Representation 8

1.3.4 The Model Using Fixed Set of Features and Standard Dataset 11

1.3.4.1 Performance of Classifiers With Feature Selection 11

1.4 Performance Metrics and Evaluation of Classifiers 13

1.4.1 Cardiac Disease Prediction Through Flexi Intra Group Selection Model 13

1.4.2 HRV Model With Flexi Set of Features 14

1.4.3 Performance of the Proposed Modified With ISM-24 15

1.5 Discussion and Conclusion 18

1.5.1 Conclusion and Future Scope 19

References 20

2 Applications of Machine Learning Techniques in Disease Detection 23
M.S. Roobini, Sowmiya M., S. Jancy and L. Suji Helen

2.1 Introduction 24

2.1.1 Overview of Machine Learning Types 24

2.1.2 Motivation 25

2.1.3 Organization the Chapter 25

2.2 Types of Machine Learning Techniques 25

2.2.1 Supervised Learning 25

2.2.2 Classification Algorithm 25

2.2.3 Regression Analysis 26

2.2.4 Linear Regression 27

2.2.4.1 Applications of Linear Regression 27

2.2.5 KNN Algorithm 28

2.2.5.1 Working of KNN 28

2.2.5.2 Drawbacks of KNN Algorithm 29

2.2.6 Decision Tree Classification Algorithm 29

2.2.6.1 Attribute Selection Measures 29

2.2.6.2 Information Gain 29

2.2.6.3 Gain Ratio 29

2.2.7 Random Forest Algorithm 29

2.2.7.1 How the Random Forest Algorithm Works 29

2.2.7.2 Advantage of Using Random Forest 30

2.2.7.3 Disadvantage of Using the Random Forest 31

2.2.8 Naive Bayes Classifier Algorithm 31

2.2.8.1 For What Reason is it Called Naive Bayes? 31

2.2.8.2 Disservices of Naive Bayes Classifier 31

2.2.9 Logistic Regression 31

2.2.9.1 Logistic Regression for Machine Learning 31

2.2.10 Support Vector Machine 32

2.2.11 Unsupervised Learning 32

2.2.11.1 Clustering 33

2.2.11.2 PCA in Machine Learning 35

2.2.12 Semi-Supervised Learning 38

2.2.12.1 What is Semi-Supervised Clustering? 38

2.2.12.2 How Semi-Supervised Learning Functions? 38

2.2.13 Reinforcement Learning 39

2.2.13.1 Artificial Intelligence 39

2.2.13.2 Deep Learning 40

2.2.13.3 Points of Interest of Machine Learning 41

2.2.13.4 Why Machine Learning is Popular 41

2.2.13.5 Test Utilizations of ML 42

2.3 Future Research Directions 43

2.3.1 Privacy 43

2.3.2 Accuracy 43

References 43

3 Dengue Incidence Rate Prediction Using Nonlinear Autoregressive Neural Network Time Series Model 47
S. Dhamodharavadhani and R. Rathipriya

3.1 Introduction 47

3.2 Related Literature Study 48

3.2.1 Limitations of Existing Works 50

3.2.2 Contributions of Proposed Methodology 50

3.3 Methods and Materials 50

3.3.1 NAR-NNTS 50

3.3.2 Fit/Train the Model 51

3.3.3 Training Algorithms 54

3.3.3.1 Levenberg-Marquardt (LM) Algorithm 54

3.3.3.2 Bayesian Regularization (BR) Algorithm 55

3.3.3.3 Scaled Conjugate Gradient (SCG) Algorithm 55

3.3.4 DIR Prediction 55

3.4 Result Discussions 56

3.4.1 Dataset Description 56

3.4.2 Evaluation Measure for NAR-NNTS Models 57

3.4.3 Analysis of Results 57

3.5 Conclusion and Future Work 65

Acknowledgment 66

References 66

4 Early Detection of Breast Cancer Using Machine Learning 69
G. Lavanya and G. Thilagavathi

4.1 Introduction 70

4.1.1 Objective 70

4.1.2 Anatomy of Breast 70

4.1.3 Breast Imaging Modalities 71

4.2 Methodology 71

4.2.1 Database 71

4.2.2 Image Pre-Processing 71

4.3 Segmentation 72

4.4 Feature Extraction 72

4.5 Classification 72

4.5.1 Naive Bayes Neural Network Classifier 72

4.5.2 Radial Basis Function Neural Network 73

4.5.2.1 Input 73

4.5.2.2 Hidden Layer 73

4.5.2.3 Output Nodes 74

4.6 Performance Evaluation Methods 74

4.7 Output 75

4.7.1 Dataset 75

4.7.2 Pre-Processing 75

4.7.3 Segmentation 75

4.7.4 Geometric Feature Extraction 77

4.8 Results and Discussion 78

4.8.1 Database 78

4.9 Conclusion and Future Scope 81

References 81

5 Machine Learning Approach for Prediction of Lung Cancer 83
Hemant Kasturiwale, Swati Bhisikar and Sandhya Save

5.1 Introduction 84

5.1.1 Disorders in Lungs 84

5.1.2 Background 84

5.1.3 Material, Datasets, and Techniques 85

5.2 Feature Extraction and Lung Cancer Analysis 86

5.3 Methodology 87

5.3.1 Proposed Algorithm Steps 87

5.3.2 Classifiers in Concurrence With Datasets 88

5.4 Proposed System and Implementation 89

5.4.1 Interpretation via Artificial Intelligence 89

5.4.2 Training of Model 90

5.4.3 Implementation and Results 90

5.5 Conclusion 99

5.5.1 Future Scope 99

References 100

6 Segmentation of Liver Tumor Using ANN 103
Hema L. K. and R. Indumathi

6.1 Introduction 103

6.2 Liver Tumor 104

6.2.1 Overview of Liver Tumor 104

6.2.2 Classification 105

6.2.2.1 Benign 105

6.2.2.2 Malignant 107

6.3 Benefits of CT to Diagnose Liver Cancer 108

6.4 Literature Review 108

6.5 Interactive Liver Tumor Segmentation by Deep Learning 109

6.6 Existing System 109

6.7 Proposed System 110

6.7.1 Pre-Processing 110

6.7.2 Segmentation 111

6.7.3 Feature Extraction 112

6.7.4 GLCM 112

6.7.5 Backpropagation Network 113

6.8 Result and Discussion 113

6.8.1 Processed Images 114

6.8.2 Segmentation 116

6.9 Future Enhancements 117

6.10 Conclusion 118

References 118

7 DMSAN: Deep Multi-Scale Attention Network for Automatic Liver Segmentation From Abdomen CT Images 121
Devidas T. Kushnure and Sanjay N. Talbar

7.1 Introduction 121

7.2 Related Work 122

7.3 Methodology 123

7.3.1 Proposed Architecture 123

7.3.2 Multi-Scale Feature Characterization Using Res2Net Module 125

7.4 Experimental Analysis 126

7.4.1 Dataset Description 126

7.4.2 Pre-Processing Dataset 127

7.4.3 Training Strategy 128

7.4.4 Loss Function 128

7.4.5 Implementation Platform 129

7.4.6 Data Augmentation 129

7.4.7 Performance Metrics 129

7.5 Results 131

7.6 Result Comparison With Other Methods 135

7.7 Discussion 136

7.8 Conclusion 137

Acknowledgement 138

References 138

8 AI-Based Identification and Prediction of Cardiac Disorders 141
Rajesh Karhe, Hemant Kasturiwale and Sujata N. Kale

8.1 Introduction 142

8.1.1 Cardiac Electrophysiology and Electrocardiogram 143

8.1.2 Heart Arrhythmia 144

8.1.2.1 Types of Arrhythmias 145

8.1.3 ECG Database 147

8.1.3.1 Association for the Advancement of Medical Instrumentation (AAMI) Standard 147

8.1.4 An Overview of ECG Signal Analysis 148

8.2 Related Work 149

8.3 Classifiers and Methodology 151

8.3.1 Databases for Cardiac Arrhythmia Detection 152

8.3.2 MIT-BIH Normal Sinus Rhythm and Arrhythmia Database 152

8.3.3 Arrhythmia Detection and Classification 153

8.3.4 Methodology 153

8.3.4.1 Database Gathering and Pre-Processing 153

8.3.4.2 QRST Wave Detection 153

8.3.4.3 Features Extraction 154

8.3.4.4 Neural Network 155

8.3.4.5 Performance Evaluation 156

8.4 Result Analysis 156

8.4.1 Arrhythmia Detection and Classification 156

8.4.2 Dataset 156

8.4.3 Evaluations and Results 156

8.4.4 Evaluating the Performance of Various Neural Network Classifiers (Arrhythmia Detection) 157

8.5 Conclusions and Future Scope 159

8.5.1 Arrhythmia Detection and Classification 159

8.5.2 Future Scope 161

References 161

9 An Implementation of Image Processing Technique for Bone Fracture Detection Including Classification 165
Rocky Upadhyay, Prakash Singh Tanwar and Sheshang Degadwala

9.1 Introduction 165

9.2 Existing Technology 166

9.2.1 Pre-Processing 166

9.2.2 Denoise Image 167

9.2.3 Histogram 168

9.3 Image Processing 169

9.3.1 Canny Edge 169

9.4 Overview of System and Steps 170

9.4.1 Workflow 170

9.4.2 Classifiers 171

9.4.2.1 Extra Tree Ensemble Method 171

9.4.2.2 SVM 172

9.4.2.3 Trained Algorithm 173

9.4.3 Feature Extraction 173

9.5 Results 174

9.5.1 Result Analysis 175

9.6 Conclusion 176

References 176

10 Improved Otsu Algorithm for Segmentation of Malaria Parasite Images 179
Mosam K. Sangole, Sanjay T. Gandhe and Dipak P. Patil

10.1 Introduction 179

10.2 Literature Review 180

10.3 Related Works 182

10.4 Proposed Algorithm 183

10.5 Experimental Results 184

10.6 Conclusion 193

References 193

11 A Reliable and Fully Automated Diagnosis of COVID-19 Based on Computed Tomography 195
Bramah Hazela, Saad Bin Khalid and Pallavi Asthana

11.1 Introduction 196

11.2 Background 196

11.3 Methodology 199

11.3.1 Models Used 199

11.3.2 Architecture of the Image Source Classification Model 199

11.3.3 Architecture of the CT Scan Classification Model 200

11.3.4 Architecture of the Ultrasound Image Classification Model 201

11.3.5 Architecture of the X-Ray Classification Model 201

11.3.6 Dataset 202

11.3.6.1 Training 202

11.4 Results 204

11.5 Conclusion 206

References 207

12 Multimodality Medical Images for Healthcare Disease Analysis 209
B. Rajalingam, R. Santhoshkumar, P. Santosh Kumar Patra, M. Narayanan, G. Govinda Rajulu and T. Poongothai

12.1 Introduction 210

12.1.1 Background 210

12.2 Brief Survey of Earlier Works 212

12.3 Medical Imaging Modalities 213

12.3.1 Computed Tomography (CT) 214

12.3.2 Magnetic Resonance Imaging (MRI) 214

12.3.3 Positron Emission Tomography (PET) 214

12.3.4 Single-Photon Emission Computed Tomography (SPECT) 215

12.4 Image Fusion 216

12.4.1 Different Levels of Image Fusion 216

12.4.1.1 Pixel Level Fusion 216

12.4.1.2 Feature Level Fusion 217

12.4.1.3 Decision Level Fusion 217

12.5 Clinical Relevance for Medical Image Fusion 218

12.5.1 Clinical Relevance for Neurocyticercosis (NCC) 218

12.5.2 Clinical Relevance for Neoplastic Disease 218

12.5.2.1 Clinical Relevance for Astrocytoma 218

12.5.2.2 Clinical Relevance for Anaplastic Astrocytoma 219

12.5.2.3 Clinical Relevance for Metastatic Bronchogenic Carcinoma 220

12.5.3 Clinical Relevance for Alzheimer’s Disease 221

12.6 Data Sets and Softwares Used 221

12.7 Generalized Image Fusion Scheme 221

12.7.1 Input Image Modalities 222

12.7.2 Image Registration 222

12.7.3 Fusion Process 223

12.7.4 Fusion Rule 223

12.7.5 Evaluation 224

12.7.5.1 Subjective Evaluation 224

12.7.5.2 Objective Evaluation 224

12.8 Medical Image Fusion Methods 224

12.8.1 Traditional Image Fusion Techniques 224

12.8.1.1 Spatial Domain Image Fusion Approach 225

12.8.1.2 Transform Domain Image Fusion Approach 225

12.8.1.3 Fuzzy Logic–Based Image Fusion Approach 227

12.8.1.4 Filtering Technique–Based Image Fusion Approach 227

12.8.1.5 Neural Network–Based Image Fusion Approach 227

12.8.2 Hybrid Image Fusion Techniques 228

12.8.2.1 Transforms with Fuzzy Logic–Based Medical Image Fusion 228

12.8.2.2 Transforms With Guided Image Filtering–Based Medical Image Fusion 229

12.8.2.3 Transforms With Neural Network–Based Image Fusion 229

12.9 Conclusions 233

12.9.1 Future Work 234

References 234

13 Health Detection System for COVID-19 Patients Using IoT 237
Dipak P. Patil, Kishor Badane, Amit Kumar Mishra and Vishal A. Wankhede

13.1 Introduction 237

13.1.1 Overview 237

13.1.2 Preventions 238

13.1.3 Symptoms 238

13.1.4 Present Situation 238

13.2 Related Works 239

13.3 System Design 239

13.3.1 Hardware Implementation 239

13.3.1.1 NodeMCU 240

13.3.1.2 DHT 11 Sensor 240

13.3.1.3 MAX30100 Oxygen Sensor 241

13.3.1.4 ThingSpeak Server 242

13.3.1.5 Arduino IDE 243

13.4 Proposed System for Detection of Corona Patients 245

13.4.1 Introduction 245

13.4.2 Arduino IDE 246

13.4.3 Hardware Implementation 246

13.5 Results and Performance Analysis 247

13.5.1 Hardware Implementation 247

13.5.1.1 Implementation of NodeMCU With Temperature Sensor 247

13.5.2 Software Implementation 248

13.5.2.1 Simulation of Temperature Sensor With Arduino on Proteus Software 248

13.5.2.2 Interfacing of LCD With Arduino 250

13.6 Conclusion 250

References 250

14 Intelligent Systems in Healthcare 253
Rajiv Dey and Pankaj Sahu

14.1 Introduction 253

14.2 Brain Computer Interface 255

14.2.1 Types of Signals Used in BCI 256

14.2.2 Components of BCI 257

14.2.3 Applications of BCI in Health Monitoring 258

14.3 Robotic Systems 258

14.3.1 Advantages of Surgical Robots 258

14.3.2 Centralization of the Important Information to the Surgeon 259

14.3.3 Remote-Surgery, Software Development, and High Speed

Connectivity Such as 5G 260

14.4 Voice Recognition Systems 260

14.5 Remote Health Monitoring Systems 260

14.5.1 Tele-Medicine Health Concerns 262

14.6 Internet of Things–Based Intelligent Systems 262

14.6.1 Ubiquitous Computing Technologies in Healthcare 264

14.6.2 Patient Bio-Signals and Acquisition Methods 265

14.6.3 Communication Technologies Used in Healthcare Application 267

14.6.4 Communication Technologies Based on Location/Position 269

14.7 Intelligent Electronic Healthcare Systems 270

14.7.1 The Background of Electronic Healthcare Systems 270

14.7.2 Intelligent Agents in Electronic Healthcare System 270

14.7.3 Patient Data Classification Techniques 271

14.8 Conclusion 271

References 272

15 Design of Antennas for Microwave Imaging Techniques 275
Dnyaneshwar D. Ahire, Gajanan K. Kharate and Ammar Muthana

15.1 Introduction 275

15.1.1 Overview 276

15.2 Literature 277

15.2.1 Microstrip Patch Antenna 278

15.2.2 Early Detection of Breast Cancer and Microstrip Patch Antenna for Biomedical Application 279

15.2.3 UWB for Microwave Imaging 279

15.3 Design and Development of Wideband Antenna 280

15.3.1 Overview 280

15.3.2 Design of Rectangular Microstrip Patch Antenna 281

15.3.3 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna 283

15.3.4 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground 285

15.3.5 Key Shape Monopole Rectangular Microstrip Patch Antenna With Rounded Corner in Partial Ground 286

15.4 Results and Inferences 290

15.4.1 Overview 290

15.4.2 Rectangular Microstrip Patch Antenna 290

15.4.2.1 Reflection and VSWR Bandwidth 290

15.4.2.2 Surface Current Distribution 291

15.4.3 Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground 292

15.4.3.1 Reflection and VSWR Bandwidth 292

15.4.3.2 Surface Current Distribution 292

15.4.3.3 Inference 293

15.4.4 Key Shape Monopole Rectangular Microstrip Patch Antenna with Rounded Corner in Partial Ground 294

15.4.4.1 Reflection and VSWR Bandwidth 294

15.4.4.2 Surface Current Distribution 294

15.4.4.3 Results of the Fabricated Antenna 295

15.4.4.4 Inference 296

15.5 Conclusion 297

References 298

16 COVID-19: A Global Crisis 303
Savita Mandan and Durgeshwari Kalal

16.1 Introduction 303

16.1.1 Structure 304

16.1.2 Classification of Corona Virus 304

16.1.3 Types of Human Coronavirus 304

16.1.4 Genome Organization of Corona Virus 305

16.1.5 Coronavirus Replication 305

16.1.6 Host Defenses 306

16.2 Clinical Manifestation and Pathogenesis 306

16.2.1 Symptoms 307

16.2.2 Epidemiology 307

16.3 Diagnosis and Control 308

16.3.1 Molecular Test 308

16.3.2 Serology 308

16.3.3 Concerning Lab Assessments 309

16.3.4 Significantly Improved D-Dimer 309

16.3.5 Imaging 309

16.3.6 HRCT 309

16.3.7 Lung Ultrasound 310

16.4 Control Measures 310

16.4.1 Prevention and Patient Education 311

16.5 Immunization 312

16.5.1 Medications 312

16.6 Conclusion 313

References 313

17 Smart Healthcare for Pregnant Women in Rural Areas 317
D. Shanthi

17.1 Introduction 317

17.2 National/International Surveys Reviews 319

17.2.1 National Family Health Survey Review-11 319

17.2.2 National Family Health Survey Review-2.2 319

17.2.3 National Family Health Survey Reviews-3 320

17.3 Architecture 320

17.4 Anganwadi’s Collaborative Work 321

17.5 Schemes Offered by Central/State Governments 321

17.5.1 AAH (Anna Amrutha Hastham) 321

17.5.2 Programme Arogya Laxmi 323

17.5.3 Balamrutham-Kids’ Weaning Food from 7 Months to 3 Years 323

17.5.4 Nutri TASC (Tracking of Group Responsibility for Services) 323

17.5.5 Akshyapatra Foundation (ISKCON) 324

17.5.6 Mahila Sishu Chaitanyam 324

17.5.7 Community Management of Acute Malnutrition 325

17.5.8 Child Health Nutrition Committee 325

17.5.9 Bharat Ratna APJ Abdul Kalam Amrut Yojna 325

17.6 Smart Healthcare System 326

17.7 Data Collection 328

17.8 Hardware and Software Features of HCS 328

17.9 Implementation 329

17.9.1 Modules 329

17.9.2 Modules Description 329

17.9.2.1 Data Preprocessing 329

17.9.2.2 Component Features Extraction 329

17.9.2.3 User Sentimental Measurement 330

17.9.2.4 Sentiment Evaluation 330

17.10 Results and Analysis 331

17.11 Conclusion 333

References 333

18 Computer-Aided Interpretation of ECG Signal—A Challenge 335
Shalini Sahay and A.K. Wadhwani

18.1 Introduction 336

18.1.1 Electrical Activity of the Heart 336

18.2 The Cardiovascular System 338

18.3 Electrocardiogram Leads 340

18.4 Artifacts/Noises Affecting the ECG 342

18.4.1 Baseline Wander 343

18.4.2 Power Line Interference 343

18.4.3 Motion Artifacts 344

18.4.4 Muscle Noise 344

18.4.5 Instrumentation Noise 344

18.4.6 Other Interferences 345

18.5 The ECG Waveform 346

18.5.1 Normal Sinus Rhythm 347

18.6 Cardiac Arrhythmias 347

18.6.1 Sinus Bradycardia 347

18.6.2 Sinus Tachycardia 348

18.6.3 Atrial Flutter 348

18.6.4 Atrial Fibrillation 349

18.6.5 Ventric ular Tachycardia 349

18.6.6 AV Block 2 First Degree 350

18.6.7 Asystole 350

18.7 Electrocardiogram Databases 351

18.8 Computer-Aided Interpretation (CAD) 351

18.9 Computational Techniques 354

18.10 Conclusion 356

References 357

Index 359