Details

Medical Imaging and Health Informatics

Next Generation Computing and Communication Engineering 1. Aufl.

von: Tushar H. Jaware, K. Sarat Kumar, Ravindra D. Badgujar, Svetlin Antonov
211,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	26.05.2022
ISBN/EAN:	9781119819141
Sprache:	englisch
Anzahl Seiten:	384

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Titelbeschreibung

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.

Inhaltsverzeichnis

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

Autorenportrait

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.

Back cover copy

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.