Medical Imaging and Health Informatics

Inbunden, Engelska, 2022

Del i serien Next Generation Computing and Communication Engineering

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Beskrivning

MEDICAL IMAGING AND HEALTH INFORMATICSProvides 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.AudienceThe core audience comprises researchers and industry engineers, scientists, radiologists, healthcare professionals, data scientists who work in health informatics, computer vision and medical image analysis.

Produktinformation

Utgivningsdatum:2022-08-29
Mått:10 x 10 x 10 mm
Vikt:454 g
Format:Inbunden
Språk:Engelska
Serie:Next Generation Computing and Communication Engineering
Antal sidor:384
Förlag:John Wiley & Sons Inc
ISBN:9781119819134

Utforska kategorier

Hälsoinformatik inom Medicin
Biokemisk teknik inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT

Mer om författaren

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.

Innehållsförteckning

Preface xvii1 Machine Learning Approach for Medical Diagnosis Based on Prediction Model 1Hemant Kasturiwale, Rajesh Karhe and Sujata N. Kale1.1 Introduction 21.1.1 Heart System and Major Cardiac Diseases 21.1.2 ECG for Heart Rate Variability Analysis 21.1.3 HRV for Cardiac Analysis 31.2 Machine Learning Approach and Prediction 31.3 Material and Experimentation 41.3.1 Data and HRV 41.3.1.1 HRV Data Analysis via ECG Data Acquisition System 51.3.2 Methodology and Techniques 61.3.2.1 Classifiers and Performance Evaluation 71.3.3 Proposed Model With Layer Representation 81.3.4 The Model Using Fixed Set of Features and Standard Dataset 111.3.4.1 Performance of Classifiers With Feature Selection 111.4 Performance Metrics and Evaluation of Classifiers 131.4.1 Cardiac Disease Prediction Through Flexi Intra Group Selection Model 131.4.2 HRV Model With Flexi Set of Features 141.4.3 Performance of the Proposed Modified With ISM-24 151.5 Discussion and Conclusion 181.5.1 Conclusion and Future Scope 19References 202 Applications of Machine Learning Techniques in Disease Detection 23M.S. Roobini, Sowmiya M., S. Jancy and L. Suji Helen2.1 Introduction 242.1.1 Overview of Machine Learning Types 242.1.2 Motivation 252.1.3 Organization the Chapter 252.2 Types of Machine Learning Techniques 252.2.1 Supervised Learning 252.2.2 Classification Algorithm 252.2.3 Regression Analysis 262.2.4 Linear Regression 272.2.4.1 Applications of Linear Regression 272.2.5 KNN Algorithm 282.2.5.1 Working of KNN 282.2.5.2 Drawbacks of KNN Algorithm 292.2.6 Decision Tree Classification Algorithm 292.2.6.1 Attribute Selection Measures 292.2.6.2 Information Gain 292.2.6.3 Gain Ratio 292.2.7 Random Forest Algorithm 292.2.7.1 How the Random Forest Algorithm Works 292.2.7.2 Advantage of Using Random Forest 302.2.7.3 Disadvantage of Using the Random Forest 312.2.8 Naive Bayes Classifier Algorithm 312.2.8.1 For What Reason is it Called Naive Bayes? 312.2.8.2 Disservices of Naive Bayes Classifier 312.2.9 Logistic Regression 312.2.9.1 Logistic Regression for Machine Learning 312.2.10 Support Vector Machine 322.2.11 Unsupervised Learning 322.2.11.1 Clustering 332.2.11.2 PCA in Machine Learning 352.2.12 Semi-Supervised Learning 382.2.12.1 What is Semi-Supervised Clustering? 382.2.12.2 How Semi-Supervised Learning Functions? 382.2.13 Reinforcement Learning 392.2.13.1 Artificial Intelligence 392.2.13.2 Deep Learning 402.2.13.3 Points of Interest of Machine Learning 412.2.13.4 Why Machine Learning is Popular 412.2.13.5 Test Utilizations of ML 422.3 Future Research Directions 432.3.1 Privacy 432.3.2 Accuracy 43References 433 Dengue Incidence Rate Prediction Using Nonlinear Autoregressive Neural Network Time Series Model 47S. Dhamodharavadhani and R. Rathipriya3.1 Introduction 473.2 Related Literature Study 483.2.1 Limitations of Existing Works 503.2.2 Contributions of Proposed Methodology 503.3 Methods and Materials 503.3.1 NAR-NNTS 503.3.2 Fit/Train the Model 513.3.3 Training Algorithms 543.3.3.1 Levenberg-Marquardt (LM) Algorithm 543.3.3.2 Bayesian Regularization (BR) Algorithm 553.3.3.3 Scaled Conjugate Gradient (SCG) Algorithm 553.3.4 DIR Prediction 553.4 Result Discussions 563.4.1 Dataset Description 563.4.2 Evaluation Measure for NAR-NNTS Models 573.4.3 Analysis of Results 573.5 Conclusion and Future Work 65Acknowledgment 66References 664 Early Detection of Breast Cancer Using Machine Learning 69G. Lavanya and G. Thilagavathi4.1 Introduction 704.1.1 Objective 704.1.2 Anatomy of Breast 704.1.3 Breast Imaging Modalities 714.2 Methodology 714.2.1 Database 714.2.2 Image Pre-Processing 714.3 Segmentation 724.4 Feature Extraction 724.5 Classification 724.5.1 Naive Bayes Neural Network Classifier 724.5.2 Radial Basis Function Neural Network 734.5.2.1 Input 734.5.2.2 Hidden Layer 734.5.2.3 Output Nodes 744.6 Performance Evaluation Methods 744.7 Output 754.7.1 Dataset 754.7.2 Pre-Processing 754.7.3 Segmentation 754.7.4 Geometric Feature Extraction 774.8 Results and Discussion 784.8.1 Database 784.9 Conclusion and Future Scope 81References 815 Machine Learning Approach for Prediction of Lung Cancer 83Hemant Kasturiwale, Swati Bhisikar and Sandhya Save5.1 Introduction 845.1.1 Disorders in Lungs 845.1.2 Background 845.1.3 Material, Datasets, and Techniques 855.2 Feature Extraction and Lung Cancer Analysis 865.3 Methodology 875.3.1 Proposed Algorithm Steps 875.3.2 Classifiers in Concurrence With Datasets 885.4 Proposed System and Implementation 895.4.1 Interpretation via Artificial Intelligence 895.4.2 Training of Model 905.4.3 Implementation and Results 905.5 Conclusion 995.5.1 Future Scope 99References 1006 Segmentation of Liver Tumor Using ANN 103Hema L. K. and R. Indumathi6.1 Introduction 1036.2 Liver Tumor 1046.2.1 Overview of Liver Tumor 1046.2.2 Classification 1056.2.2.1 Benign 1056.2.2.2 Malignant 1076.3 Benefits of CT to Diagnose Liver Cancer 1086.4 Literature Review 1086.5 Interactive Liver Tumor Segmentation by Deep Learning 1096.6 Existing System 1096.7 Proposed System 1106.7.1 Pre-Processing 1106.7.2 Segmentation 1116.7.3 Feature Extraction 1126.7.4 GLCM 1126.7.5 Backpropagation Network 1136.8 Result and Discussion 1136.8.1 Processed Images 1146.8.2 Segmentation 1166.9 Future Enhancements 1176.10 Conclusion 118References 1187 DMSAN: Deep Multi-Scale Attention Network for Automatic Liver Segmentation From Abdomen CT Images 121Devidas T. Kushnure and Sanjay N. Talbar7.1 Introduction 1217.2 Related Work 1227.3 Methodology 1237.3.1 Proposed Architecture 1237.3.2 Multi-Scale Feature Characterization Using Res2Net Module 1257.4 Experimental Analysis 1267.4.1 Dataset Description 1267.4.2 Pre-Processing Dataset 1277.4.3 Training Strategy 1287.4.4 Loss Function 1287.4.5 Implementation Platform 1297.4.6 Data Augmentation 1297.4.7 Performance Metrics 1297.5 Results 1317.6 Result Comparison With Other Methods 1357.7 Discussion 1367.8 Conclusion 137Acknowledgement 138References 1388 AI-Based Identification and Prediction of Cardiac Disorders 141Rajesh Karhe, Hemant Kasturiwale and Sujata N. Kale8.1 Introduction 1428.1.1 Cardiac Electrophysiology and Electrocardiogram 1438.1.2 Heart Arrhythmia 1448.1.2.1 Types of Arrhythmias 1458.1.3 ECG Database 1478.1.3.1 Association for the Advancement of Medical Instrumentation (AAMI) Standard 1478.1.4 An Overview of ECG Signal Analysis 1488.2 Related Work 1498.3 Classifiers and Methodology 1518.3.1 Databases for Cardiac Arrhythmia Detection 1528.3.2 MIT-BIH Normal Sinus Rhythm and Arrhythmia Database 1528.3.3 Arrhythmia Detection and Classification 1538.3.4 Methodology 1538.3.4.1 Database Gathering and Pre-Processing 1538.3.4.2 QRST Wave Detection 1538.3.4.3 Features Extraction 1548.3.4.4 Neural Network 1558.3.4.5 Performance Evaluation 1568.4 Result Analysis 1568.4.1 Arrhythmia Detection and Classification 1568.4.2 Dataset 1568.4.3 Evaluations and Results 1568.4.4 Evaluating the Performance of Various Neural Network Classifiers (Arrhythmia Detection) 1578.5 Conclusions and Future Scope 1598.5.1 Arrhythmia Detection and Classification 1598.5.2 Future Scope 161References 1619 An Implementation of Image Processing Technique for Bone Fracture Detection Including Classification 165Rocky Upadhyay, Prakash Singh Tanwar and Sheshang Degadwala9.1 Introduction 1659.2 Existing Technology 1669.2.1 Pre-Processing 1669.2.2 Denoise Image 1679.2.3 Histogram 1689.3 Image Processing 1699.3.1 Canny Edge 1699.4 Overview of System and Steps 1709.4.1 Workflow 1709.4.2 Classifiers 1719.4.2.1 Extra Tree Ensemble Method 1719.4.2.2 SVM 1729.4.2.3 Trained Algorithm 1739.4.3 Feature Extraction 1739.5 Results 1749.5.1 Result Analysis 1759.6 Conclusion 176References 17610 Improved Otsu Algorithm for Segmentation of Malaria Parasite Images 179Mosam K. Sangole, Sanjay T. Gandhe and Dipak P. Patil10.1 Introduction 17910.2 Literature Review 18010.3 Related Works 18210.4 Proposed Algorithm 18310.5 Experimental Results 18410.6 Conclusion 193References 19311 A Reliable and Fully Automated Diagnosis of COVID-19 Based on Computed Tomography 195Bramah Hazela, Saad Bin Khalid and Pallavi Asthana11.1 Introduction 19611.2 Background 19611.3 Methodology 19911.3.1 Models Used 19911.3.2 Architecture of the Image Source Classification Model 19911.3.3 Architecture of the CT Scan Classification Model 20011.3.4 Architecture of the Ultrasound Image Classification Model 20111.3.5 Architecture of the X-Ray Classification Model 20111.3.6 Dataset 20211.3.6.1 Training 20211.4 Results 20411.5 Conclusion 206References 20712 Multimodality Medical Images for Healthcare Disease Analysis 209B. Rajalingam, R. Santhoshkumar, P. Santosh Kumar Patra, M. Narayanan, G. Govinda Rajulu and T. Poongothai12.1 Introduction 21012.1.1 Background 21012.2 Brief Survey of Earlier Works 21212.3 Medical Imaging Modalities 21312.3.1 Computed Tomography (CT) 21412.3.2 Magnetic Resonance Imaging (MRI) 21412.3.3 Positron Emission Tomography (PET) 21412.3.4 Single-Photon Emission Computed Tomography (SPECT) 21512.4 Image Fusion 21612.4.1 Different Levels of Image Fusion 21612.4.1.1 Pixel Level Fusion 21612.4.1.2 Feature Level Fusion 21712.4.1.3 Decision Level Fusion 21712.5 Clinical Relevance for Medical Image Fusion 21812.5.1 Clinical Relevance for Neurocyticercosis (NCC) 21812.5.2 Clinical Relevance for Neoplastic Disease 21812.5.2.1 Clinical Relevance for Astrocytoma 21812.5.2.2 Clinical Relevance for Anaplastic Astrocytoma 21912.5.2.3 Clinical Relevance for Metastatic Bronchogenic Carcinoma 22012.5.3 Clinical Relevance for Alzheimer’s Disease 22112.6 Data Sets and Softwares Used 22112.7 Generalized Image Fusion Scheme 22112.7.1 Input Image Modalities 22212.7.2 Image Registration 22212.7.3 Fusion Process 22312.7.4 Fusion Rule 22312.7.5 Evaluation 22412.7.5.1 Subjective Evaluation 22412.7.5.2 Objective Evaluation 22412.8 Medical Image Fusion Methods 22412.8.1 Traditional Image Fusion Techniques 22412.8.1.1 Spatial Domain Image Fusion Approach 22512.8.1.2 Transform Domain Image Fusion Approach 22512.8.1.3 Fuzzy Logic–Based Image Fusion Approach 22712.8.1.4 Filtering Technique–Based Image Fusion Approach 22712.8.1.5 Neural Network–Based Image Fusion Approach 22712.8.2 Hybrid Image Fusion Techniques 22812.8.2.1 Transforms with Fuzzy Logic–Based Medical Image Fusion 22812.8.2.2 Transforms With Guided Image Filtering–Based Medical Image Fusion 22912.8.2.3 Transforms With Neural Network–Based Image Fusion 22912.9 Conclusions 23312.9.1 Future Work 234References 23413 Health Detection System for COVID-19 Patients Using IoT 237Dipak P. Patil, Kishor Badane, Amit Kumar Mishra and Vishal A. Wankhede13.1 Introduction 23713.1.1 Overview 23713.1.2 Preventions 23813.1.3 Symptoms 23813.1.4 Present Situation 23813.2 Related Works 23913.3 System Design 23913.3.1 Hardware Implementation 23913.3.1.1 NodeMCU 24013.3.1.2 DHT 11 Sensor 24013.3.1.3 MAX30100 Oxygen Sensor 24113.3.1.4 ThingSpeak Server 24213.3.1.5 Arduino IDE 24313.4 Proposed System for Detection of Corona Patients 24513.4.1 Introduction 24513.4.2 Arduino IDE 24613.4.3 Hardware Implementation 24613.5 Results and Performance Analysis 24713.5.1 Hardware Implementation 24713.5.1.1 Implementation of NodeMCU With Temperature Sensor 24713.5.2 Software Implementation 24813.5.2.1 Simulation of Temperature Sensor With Arduino on Proteus Software 24813.5.2.2 Interfacing of LCD With Arduino 25013.6 Conclusion 250References 25014 Intelligent Systems in Healthcare 253Rajiv Dey and Pankaj Sahu14.1 Introduction 25314.2 Brain Computer Interface 25514.2.1 Types of Signals Used in BCI 25614.2.2 Components of BCI 25714.2.3 Applications of BCI in Health Monitoring 25814.3 Robotic Systems 25814.3.1 Advantages of Surgical Robots 25814.3.2 Centralization of the Important Information to the Surgeon 25914.3.3 Remote-Surgery, Software Development, and High SpeedConnectivity Such as 5G 26014.4 Voice Recognition Systems 26014.5 Remote Health Monitoring Systems 26014.5.1 Tele-Medicine Health Concerns 26214.6 Internet of Things–Based Intelligent Systems 26214.6.1 Ubiquitous Computing Technologies in Healthcare 26414.6.2 Patient Bio-Signals and Acquisition Methods 26514.6.3 Communication Technologies Used in Healthcare Application 26714.6.4 Communication Technologies Based on Location/Position 26914.7 Intelligent Electronic Healthcare Systems 27014.7.1 The Background of Electronic Healthcare Systems 27014.7.2 Intelligent Agents in Electronic Healthcare System 27014.7.3 Patient Data Classification Techniques 27114.8 Conclusion 271References 27215 Design of Antennas for Microwave Imaging Techniques 275Dnyaneshwar D. Ahire, Gajanan K. Kharate and Ammar Muthana15.1 Introduction 27515.1.1 Overview 27615.2 Literature 27715.2.1 Microstrip Patch Antenna 27815.2.2 Early Detection of Breast Cancer and Microstrip Patch Antenna for Biomedical Application 27915.2.3 UWB for Microwave Imaging 27915.3 Design and Development of Wideband Antenna 28015.3.1 Overview 28015.3.2 Design of Rectangular Microstrip Patch Antenna 28115.3.3 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna 28315.3.4 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground 28515.3.5 Key Shape Monopole Rectangular Microstrip Patch Antenna With Rounded Corner in Partial Ground 28615.4 Results and Inferences 29015.4.1 Overview 29015.4.2 Rectangular Microstrip Patch Antenna 29015.4.2.1 Reflection and VSWR Bandwidth 29015.4.2.2 Surface Current Distribution 29115.4.3 Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground 29215.4.3.1 Reflection and VSWR Bandwidth 29215.4.3.2 Surface Current Distribution 29215.4.3.3 Inference 29315.4.4 Key Shape Monopole Rectangular Microstrip Patch Antenna with Rounded Corner in Partial Ground 29415.4.4.1 Reflection and VSWR Bandwidth 29415.4.4.2 Surface Current Distribution 29415.4.4.3 Results of the Fabricated Antenna 29515.4.4.4 Inference 29615.5 Conclusion 297References 29816 COVID-19: A Global Crisis 303Savita Mandan and Durgeshwari Kalal16.1 Introduction 30316.1.1 Structure 30416.1.2 Classification of Corona Virus 30416.1.3 Types of Human Coronavirus 30416.1.4 Genome Organization of Corona Virus 30516.1.5 Coronavirus Replication 30516.1.6 Host Defenses 30616.2 Clinical Manifestation and Pathogenesis 30616.2.1 Symptoms 30716.2.2 Epidemiology 30716.3 Diagnosis and Control 30816.3.1 Molecular Test 30816.3.2 Serology 30816.3.3 Concerning Lab Assessments 30916.3.4 Significantly Improved D-Dimer 30916.3.5 Imaging 30916.3.6 HRCT 30916.3.7 Lung Ultrasound 31016.4 Control Measures 31016.4.1 Prevention and Patient Education 31116.5 Immunization 31216.5.1 Medications 31216.6 Conclusion 313References 31317 Smart Healthcare for Pregnant Women in Rural Areas 317D. Shanthi17.1 Introduction 31717.2 National/International Surveys Reviews 31917.2.1 National Family Health Survey Review-11 31917.2.2 National Family Health Survey Review-2.2 31917.2.3 National Family Health Survey Reviews-3 32017.3 Architecture 32017.4 Anganwadi’s Collaborative Work 32117.5 Schemes Offered by Central/State Governments 32117.5.1 AAH (Anna Amrutha Hastham) 32117.5.2 Programme Arogya Laxmi 32317.5.3 Balamrutham-Kids’ Weaning Food from 7 Months to 3 Years 32317.5.4 Nutri TASC (Tracking of Group Responsibility for Services) 32317.5.5 Akshyapatra Foundation (ISKCON) 32417.5.6 Mahila Sishu Chaitanyam 32417.5.7 Community Management of Acute Malnutrition 32517.5.8 Child Health Nutrition Committee 32517.5.9 Bharat Ratna APJ Abdul Kalam Amrut Yojna 32517.6 Smart Healthcare System 32617.7 Data Collection 32817.8 Hardware and Software Features of HCS 32817.9 Implementation 32917.9.1 Modules 32917.9.2 Modules Description 32917.9.2.1 Data Preprocessing 32917.9.2.2 Component Features Extraction 32917.9.2.3 User Sentimental Measurement 33017.9.2.4 Sentiment Evaluation 33017.10 Results and Analysis 33117.11 Conclusion 333References 33318 Computer-Aided Interpretation of ECG Signal—A Challenge 335Shalini Sahay and A.K. Wadhwani18.1 Introduction 33618.1.1 Electrical Activity of the Heart 33618.2 The Cardiovascular System 33818.3 Electrocardiogram Leads 34018.4 Artifacts/Noises Affecting the ECG 34218.4.1 Baseline Wander 34318.4.2 Power Line Interference 34318.4.3 Motion Artifacts 34418.4.4 Muscle Noise 34418.4.5 Instrumentation Noise 34418.4.6 Other Interferences 34518.5 The ECG Waveform 34618.5.1 Normal Sinus Rhythm 34718.6 Cardiac Arrhythmias 34718.6.1 Sinus Bradycardia 34718.6.2 Sinus Tachycardia 34818.6.3 Atrial Flutter 34818.6.4 Atrial Fibrillation 34918.6.5 Ventric ular Tachycardia 34918.6.6 AV Block 2 First Degree 35018.6.7 Asystole 35018.7 Electrocardiogram Databases 35118.8 Computer-Aided Interpretation (CAD) 35118.9 Computational Techniques 35418.10 Conclusion 356References 357Index 359

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