{"product_id":"medical-imaging-and-health-informatics-hardback-9781119819134","title":"Medical Imaging and Health Informatics (Hardback) 9781119819134","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMedical Imaging and Health Informatics\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eTushar H. Jaware (Edited by), Jaware (Author), K. Sarat Kumar (Edited by), Ravindra D. Badgujar (Edited by), Svetlin Antonov (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119819134, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 29 August 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e384 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eMEDICAL IMAGING AND HEALTH INFORMATICS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eProvides 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.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMedical 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.\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e\u003cbr\u003eThe core audience comprises researchers and industry engineers, scientists, radiologists, healthcare professionals, data scientists who work in health informatics, computer vision and medical image analysis.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003ePreface xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Machine Learning Approach for Medical Diagnosis Based on Prediction Model 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHemant Kasturiwale, Rajesh Karhe and Sujata N. Kale\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.1.1 Heart System and Major Cardiac Diseases 2\u003c\/p\u003e \u003cp\u003e1.1.2 ECG for Heart Rate Variability Analysis 2\u003c\/p\u003e \u003cp\u003e1.1.3 HRV for Cardiac Analysis 3\u003c\/p\u003e \u003cp\u003e1.2 Machine Learning Approach and Prediction 3\u003c\/p\u003e \u003cp\u003e1.3 Material and Experimentation 4\u003c\/p\u003e \u003cp\u003e1.3.1 Data and HRV 4\u003c\/p\u003e \u003cp\u003e1.3.1.1 HRV Data Analysis via ECG Data Acquisition System 5\u003c\/p\u003e \u003cp\u003e1.3.2 Methodology and Techniques 6\u003c\/p\u003e \u003cp\u003e1.3.2.1 Classifiers and Performance Evaluation 7\u003c\/p\u003e \u003cp\u003e1.3.3 Proposed Model With Layer Representation 8\u003c\/p\u003e \u003cp\u003e1.3.4 The Model Using Fixed Set of Features and Standard Dataset 11\u003c\/p\u003e \u003cp\u003e1.3.4.1 Performance of Classifiers With Feature Selection 11\u003c\/p\u003e \u003cp\u003e1.4 Performance Metrics and Evaluation of Classifiers 13\u003c\/p\u003e \u003cp\u003e1.4.1 Cardiac Disease Prediction Through Flexi Intra Group Selection Model 13\u003c\/p\u003e \u003cp\u003e1.4.2 HRV Model With Flexi Set of Features 14\u003c\/p\u003e \u003cp\u003e1.4.3 Performance of the Proposed Modified With ISM-24 15\u003c\/p\u003e \u003cp\u003e1.5 Discussion and Conclusion 18\u003c\/p\u003e \u003cp\u003e1.5.1 Conclusion and Future Scope 19\u003c\/p\u003e \u003cp\u003eReferences 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Applications of Machine Learning Techniques in Disease Detection 23\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eM.S. Roobini, Sowmiya M., S. Jancy and L. Suji Helen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 24\u003c\/p\u003e \u003cp\u003e2.1.1 Overview of Machine Learning Types 24\u003c\/p\u003e \u003cp\u003e2.1.2 Motivation 25\u003c\/p\u003e \u003cp\u003e2.1.3 Organization the Chapter 25\u003c\/p\u003e \u003cp\u003e2.2 Types of Machine Learning Techniques 25\u003c\/p\u003e \u003cp\u003e2.2.1 Supervised Learning 25\u003c\/p\u003e \u003cp\u003e2.2.2 Classification Algorithm 25\u003c\/p\u003e \u003cp\u003e2.2.3 Regression Analysis 26\u003c\/p\u003e \u003cp\u003e2.2.4 Linear Regression 27\u003c\/p\u003e \u003cp\u003e2.2.4.1 Applications of Linear Regression 27\u003c\/p\u003e \u003cp\u003e2.2.5 KNN Algorithm 28\u003c\/p\u003e \u003cp\u003e2.2.5.1 Working of KNN 28\u003c\/p\u003e \u003cp\u003e2.2.5.2 Drawbacks of KNN Algorithm 29\u003c\/p\u003e \u003cp\u003e2.2.6 Decision Tree Classification Algorithm 29\u003c\/p\u003e \u003cp\u003e2.2.6.1 Attribute Selection Measures 29\u003c\/p\u003e \u003cp\u003e2.2.6.2 Information Gain 29\u003c\/p\u003e \u003cp\u003e2.2.6.3 Gain Ratio 29\u003c\/p\u003e \u003cp\u003e2.2.7 Random Forest Algorithm 29\u003c\/p\u003e \u003cp\u003e2.2.7.1 How the Random Forest Algorithm Works 29\u003c\/p\u003e \u003cp\u003e2.2.7.2 Advantage of Using Random Forest 30\u003c\/p\u003e \u003cp\u003e2.2.7.3 Disadvantage of Using the Random Forest 31\u003c\/p\u003e \u003cp\u003e2.2.8 Naive Bayes Classifier Algorithm 31\u003c\/p\u003e \u003cp\u003e2.2.8.1 For What Reason is it Called Naive Bayes? 31\u003c\/p\u003e \u003cp\u003e2.2.8.2 Disservices of Naive Bayes Classifier 31\u003c\/p\u003e \u003cp\u003e2.2.9 Logistic Regression 31\u003c\/p\u003e \u003cp\u003e2.2.9.1 Logistic Regression for Machine Learning 31\u003c\/p\u003e \u003cp\u003e2.2.10 Support Vector Machine 32\u003c\/p\u003e \u003cp\u003e2.2.11 Unsupervised Learning 32\u003c\/p\u003e \u003cp\u003e2.2.11.1 Clustering 33\u003c\/p\u003e \u003cp\u003e2.2.11.2 PCA in Machine Learning 35\u003c\/p\u003e \u003cp\u003e2.2.12 Semi-Supervised Learning 38\u003c\/p\u003e \u003cp\u003e2.2.12.1 What is Semi-Supervised Clustering? 38\u003c\/p\u003e \u003cp\u003e2.2.12.2 How Semi-Supervised Learning Functions? 38\u003c\/p\u003e \u003cp\u003e2.2.13 Reinforcement Learning 39\u003c\/p\u003e \u003cp\u003e2.2.13.1 Artificial Intelligence 39\u003c\/p\u003e \u003cp\u003e2.2.13.2 Deep Learning 40\u003c\/p\u003e \u003cp\u003e2.2.13.3 Points of Interest of Machine Learning 41\u003c\/p\u003e \u003cp\u003e2.2.13.4 Why Machine Learning is Popular 41\u003c\/p\u003e \u003cp\u003e2.2.13.5 Test Utilizations of ML 42\u003c\/p\u003e \u003cp\u003e2.3 Future Research Directions 43\u003c\/p\u003e \u003cp\u003e2.3.1 Privacy 43\u003c\/p\u003e \u003cp\u003e2.3.2 Accuracy 43\u003c\/p\u003e \u003cp\u003eReferences 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Dengue Incidence Rate Prediction Using Nonlinear Autoregressive Neural Network Time Series Model 47\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eS. Dhamodharavadhani and R. Rathipriya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 47\u003c\/p\u003e \u003cp\u003e3.2 Related Literature Study 48\u003c\/p\u003e \u003cp\u003e3.2.1 Limitations of Existing Works 50\u003c\/p\u003e \u003cp\u003e3.2.2 Contributions of Proposed Methodology 50\u003c\/p\u003e \u003cp\u003e3.3 Methods and Materials 50\u003c\/p\u003e \u003cp\u003e3.3.1 NAR-NNTS 50\u003c\/p\u003e \u003cp\u003e3.3.2 Fit\/Train the Model 51\u003c\/p\u003e \u003cp\u003e3.3.3 Training Algorithms 54\u003c\/p\u003e \u003cp\u003e3.3.3.1 Levenberg-Marquardt (LM) Algorithm 54\u003c\/p\u003e \u003cp\u003e3.3.3.2 Bayesian Regularization (BR) Algorithm 55\u003c\/p\u003e \u003cp\u003e3.3.3.3 Scaled Conjugate Gradient (SCG) Algorithm 55\u003c\/p\u003e \u003cp\u003e3.3.4 DIR Prediction 55\u003c\/p\u003e \u003cp\u003e3.4 Result Discussions 56\u003c\/p\u003e \u003cp\u003e3.4.1 Dataset Description 56\u003c\/p\u003e \u003cp\u003e3.4.2 Evaluation Measure for NAR-NNTS Models 57\u003c\/p\u003e \u003cp\u003e3.4.3 Analysis of Results 57\u003c\/p\u003e \u003cp\u003e3.5 Conclusion and Future Work 65\u003c\/p\u003e \u003cp\u003eAcknowledgment 66\u003c\/p\u003e \u003cp\u003eReferences 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Early Detection of Breast Cancer Using Machine Learning 69\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eG. Lavanya and G. Thilagavathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 70\u003c\/p\u003e \u003cp\u003e4.1.1 Objective 70\u003c\/p\u003e \u003cp\u003e4.1.2 Anatomy of Breast 70\u003c\/p\u003e \u003cp\u003e4.1.3 Breast Imaging Modalities 71\u003c\/p\u003e \u003cp\u003e4.2 Methodology 71\u003c\/p\u003e \u003cp\u003e4.2.1 Database 71\u003c\/p\u003e \u003cp\u003e4.2.2 Image Pre-Processing 71\u003c\/p\u003e \u003cp\u003e4.3 Segmentation 72\u003c\/p\u003e \u003cp\u003e4.4 Feature Extraction 72\u003c\/p\u003e \u003cp\u003e4.5 Classification 72\u003c\/p\u003e \u003cp\u003e4.5.1 Naive Bayes Neural Network Classifier 72\u003c\/p\u003e \u003cp\u003e4.5.2 Radial Basis Function Neural Network 73\u003c\/p\u003e \u003cp\u003e4.5.2.1 Input 73\u003c\/p\u003e \u003cp\u003e4.5.2.2 Hidden Layer 73\u003c\/p\u003e \u003cp\u003e4.5.2.3 Output Nodes 74\u003c\/p\u003e \u003cp\u003e4.6 Performance Evaluation Methods 74\u003c\/p\u003e \u003cp\u003e4.7 Output 75\u003c\/p\u003e \u003cp\u003e4.7.1 Dataset 75\u003c\/p\u003e \u003cp\u003e4.7.2 Pre-Processing 75\u003c\/p\u003e \u003cp\u003e4.7.3 Segmentation 75\u003c\/p\u003e \u003cp\u003e4.7.4 Geometric Feature Extraction 77\u003c\/p\u003e \u003cp\u003e4.8 Results and Discussion 78\u003c\/p\u003e \u003cp\u003e4.8.1 Database 78\u003c\/p\u003e \u003cp\u003e4.9 Conclusion and Future Scope 81\u003c\/p\u003e \u003cp\u003eReferences 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Machine Learning Approach for Prediction of Lung Cancer 83\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHemant Kasturiwale, Swati Bhisikar and Sandhya Save\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 84\u003c\/p\u003e \u003cp\u003e5.1.1 Disorders in Lungs 84\u003c\/p\u003e \u003cp\u003e5.1.2 Background 84\u003c\/p\u003e \u003cp\u003e5.1.3 Material, Datasets, and Techniques 85\u003c\/p\u003e \u003cp\u003e5.2 Feature Extraction and Lung Cancer Analysis 86\u003c\/p\u003e \u003cp\u003e5.3 Methodology 87\u003c\/p\u003e \u003cp\u003e5.3.1 Proposed Algorithm Steps 87\u003c\/p\u003e \u003cp\u003e5.3.2 Classifiers in Concurrence With Datasets 88\u003c\/p\u003e \u003cp\u003e5.4 Proposed System and Implementation 89\u003c\/p\u003e \u003cp\u003e5.4.1 Interpretation via Artificial Intelligence 89\u003c\/p\u003e \u003cp\u003e5.4.2 Training of Model 90\u003c\/p\u003e \u003cp\u003e5.4.3 Implementation and Results 90\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 99\u003c\/p\u003e \u003cp\u003e5.5.1 Future Scope 99\u003c\/p\u003e \u003cp\u003eReferences 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Segmentation of Liver Tumor Using ANN 103\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHema L. K. and R. Indumathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 103\u003c\/p\u003e \u003cp\u003e6.2 Liver Tumor 104\u003c\/p\u003e \u003cp\u003e6.2.1 Overview of Liver Tumor 104\u003c\/p\u003e \u003cp\u003e6.2.2 Classification 105\u003c\/p\u003e \u003cp\u003e6.2.2.1 Benign 105\u003c\/p\u003e \u003cp\u003e6.2.2.2 Malignant 107\u003c\/p\u003e \u003cp\u003e6.3 Benefits of CT to Diagnose Liver Cancer 108\u003c\/p\u003e \u003cp\u003e6.4 Literature Review 108\u003c\/p\u003e \u003cp\u003e6.5 Interactive Liver Tumor Segmentation by Deep Learning 109\u003c\/p\u003e \u003cp\u003e6.6 Existing System 109\u003c\/p\u003e \u003cp\u003e6.7 Proposed System 110\u003c\/p\u003e \u003cp\u003e6.7.1 Pre-Processing 110\u003c\/p\u003e \u003cp\u003e6.7.2 Segmentation 111\u003c\/p\u003e \u003cp\u003e6.7.3 Feature Extraction 112\u003c\/p\u003e \u003cp\u003e6.7.4 GLCM 112\u003c\/p\u003e \u003cp\u003e6.7.5 Backpropagation Network 113\u003c\/p\u003e \u003cp\u003e6.8 Result and Discussion 113\u003c\/p\u003e \u003cp\u003e6.8.1 Processed Images 114\u003c\/p\u003e \u003cp\u003e6.8.2 Segmentation 116\u003c\/p\u003e \u003cp\u003e6.9 Future Enhancements 117\u003c\/p\u003e \u003cp\u003e6.10 Conclusion 118\u003c\/p\u003e \u003cp\u003eReferences 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 DMSAN: Deep Multi-Scale Attention Network for Automatic Liver Segmentation From Abdomen CT Images 121\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDevidas T. Kushnure and Sanjay N. Talbar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 121\u003c\/p\u003e \u003cp\u003e7.2 Related Work 122\u003c\/p\u003e \u003cp\u003e7.3 Methodology 123\u003c\/p\u003e \u003cp\u003e7.3.1 Proposed Architecture 123\u003c\/p\u003e \u003cp\u003e7.3.2 Multi-Scale Feature Characterization Using Res2Net Module 125\u003c\/p\u003e \u003cp\u003e7.4 Experimental Analysis 126\u003c\/p\u003e \u003cp\u003e7.4.1 Dataset Description 126\u003c\/p\u003e \u003cp\u003e7.4.2 Pre-Processing Dataset 127\u003c\/p\u003e \u003cp\u003e7.4.3 Training Strategy 128\u003c\/p\u003e \u003cp\u003e7.4.4 Loss Function 128\u003c\/p\u003e \u003cp\u003e7.4.5 Implementation Platform 129\u003c\/p\u003e \u003cp\u003e7.4.6 Data Augmentation 129\u003c\/p\u003e \u003cp\u003e7.4.7 Performance Metrics 129\u003c\/p\u003e \u003cp\u003e7.5 Results 131\u003c\/p\u003e \u003cp\u003e7.6 Result Comparison With Other Methods 135\u003c\/p\u003e \u003cp\u003e7.7 Discussion 136\u003c\/p\u003e \u003cp\u003e7.8 Conclusion 137\u003c\/p\u003e \u003cp\u003eAcknowledgement 138\u003c\/p\u003e \u003cp\u003eReferences 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 AI-Based Identification and Prediction of Cardiac Disorders 141\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRajesh Karhe, Hemant Kasturiwale and Sujata N. Kale\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 142\u003c\/p\u003e \u003cp\u003e8.1.1 Cardiac Electrophysiology and Electrocardiogram 143\u003c\/p\u003e \u003cp\u003e8.1.2 Heart Arrhythmia 144\u003c\/p\u003e \u003cp\u003e8.1.2.1 Types of Arrhythmias 145\u003c\/p\u003e \u003cp\u003e8.1.3 ECG Database 147\u003c\/p\u003e \u003cp\u003e8.1.3.1 Association for the Advancement of Medical Instrumentation (AAMI) Standard 147\u003c\/p\u003e \u003cp\u003e8.1.4 An Overview of ECG Signal Analysis 148\u003c\/p\u003e \u003cp\u003e8.2 Related Work 149\u003c\/p\u003e \u003cp\u003e8.3 Classifiers and Methodology 151\u003c\/p\u003e \u003cp\u003e8.3.1 Databases for Cardiac Arrhythmia Detection 152\u003c\/p\u003e \u003cp\u003e8.3.2 MIT-BIH Normal Sinus Rhythm and Arrhythmia Database 152\u003c\/p\u003e \u003cp\u003e8.3.3 Arrhythmia Detection and Classification 153\u003c\/p\u003e \u003cp\u003e8.3.4 Methodology 153\u003c\/p\u003e \u003cp\u003e8.3.4.1 Database Gathering and Pre-Processing 153\u003c\/p\u003e \u003cp\u003e8.3.4.2 QRST Wave Detection 153\u003c\/p\u003e \u003cp\u003e8.3.4.3 Features Extraction 154\u003c\/p\u003e \u003cp\u003e8.3.4.4 Neural Network 155\u003c\/p\u003e \u003cp\u003e8.3.4.5 Performance Evaluation 156\u003c\/p\u003e \u003cp\u003e8.4 Result Analysis 156\u003c\/p\u003e \u003cp\u003e8.4.1 Arrhythmia Detection and Classification 156\u003c\/p\u003e \u003cp\u003e8.4.2 Dataset 156\u003c\/p\u003e \u003cp\u003e8.4.3 Evaluations and Results 156\u003c\/p\u003e \u003cp\u003e8.4.4 Evaluating the Performance of Various Neural Network Classifiers (Arrhythmia Detection) 157\u003c\/p\u003e \u003cp\u003e8.5 Conclusions and Future Scope 159\u003c\/p\u003e \u003cp\u003e8.5.1 Arrhythmia Detection and Classification 159\u003c\/p\u003e \u003cp\u003e8.5.2 Future Scope 161\u003c\/p\u003e \u003cp\u003eReferences 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 An Implementation of Image Processing Technique for Bone Fracture Detection Including Classification 165\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRocky Upadhyay, Prakash Singh Tanwar and Sheshang Degadwala\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 165\u003c\/p\u003e \u003cp\u003e9.2 Existing Technology 166\u003c\/p\u003e \u003cp\u003e9.2.1 Pre-Processing 166\u003c\/p\u003e \u003cp\u003e9.2.2 Denoise Image 167\u003c\/p\u003e \u003cp\u003e9.2.3 Histogram 168\u003c\/p\u003e \u003cp\u003e9.3 Image Processing 169\u003c\/p\u003e \u003cp\u003e9.3.1 Canny Edge 169\u003c\/p\u003e \u003cp\u003e9.4 Overview of System and Steps 170\u003c\/p\u003e \u003cp\u003e9.4.1 Workflow 170\u003c\/p\u003e \u003cp\u003e9.4.2 Classifiers 171\u003c\/p\u003e \u003cp\u003e9.4.2.1 Extra Tree Ensemble Method 171\u003c\/p\u003e \u003cp\u003e9.4.2.2 SVM 172\u003c\/p\u003e \u003cp\u003e9.4.2.3 Trained Algorithm 173\u003c\/p\u003e \u003cp\u003e9.4.3 Feature Extraction 173\u003c\/p\u003e \u003cp\u003e9.5 Results 174\u003c\/p\u003e \u003cp\u003e9.5.1 Result Analysis 175\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 176\u003c\/p\u003e \u003cp\u003eReferences 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Improved Otsu Algorithm for Segmentation of Malaria Parasite Images 179\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMosam K. Sangole, Sanjay T. Gandhe and Dipak P. Patil\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 179\u003c\/p\u003e \u003cp\u003e10.2 Literature Review 180\u003c\/p\u003e \u003cp\u003e10.3 Related Works 182\u003c\/p\u003e \u003cp\u003e10.4 Proposed Algorithm 183\u003c\/p\u003e \u003cp\u003e10.5 Experimental Results 184\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 193\u003c\/p\u003e \u003cp\u003eReferences 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 A Reliable and Fully Automated Diagnosis of COVID-19 Based on Computed Tomography 195\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBramah Hazela, Saad Bin Khalid and Pallavi Asthana\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 196\u003c\/p\u003e \u003cp\u003e11.2 Background 196\u003c\/p\u003e \u003cp\u003e11.3 Methodology 199\u003c\/p\u003e \u003cp\u003e11.3.1 Models Used 199\u003c\/p\u003e \u003cp\u003e11.3.2 Architecture of the Image Source Classification Model 199\u003c\/p\u003e \u003cp\u003e11.3.3 Architecture of the CT Scan Classification Model 200\u003c\/p\u003e \u003cp\u003e11.3.4 Architecture of the Ultrasound Image Classification Model 201\u003c\/p\u003e \u003cp\u003e11.3.5 Architecture of the X-Ray Classification Model 201\u003c\/p\u003e \u003cp\u003e11.3.6 Dataset 202\u003c\/p\u003e \u003cp\u003e11.3.6.1 Training 202\u003c\/p\u003e \u003cp\u003e11.4 Results 204\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 206\u003c\/p\u003e \u003cp\u003eReferences 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Multimodality Medical Images for Healthcare Disease Analysis 209\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eB. Rajalingam, R. Santhoshkumar, P. Santosh Kumar Patra, M. Narayanan, G. Govinda Rajulu and T. Poongothai\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 210\u003c\/p\u003e \u003cp\u003e12.1.1 Background 210\u003c\/p\u003e \u003cp\u003e12.2 Brief Survey of Earlier Works 212\u003c\/p\u003e \u003cp\u003e12.3 Medical Imaging Modalities 213\u003c\/p\u003e \u003cp\u003e12.3.1 Computed Tomography (CT) 214\u003c\/p\u003e \u003cp\u003e12.3.2 Magnetic Resonance Imaging (MRI) 214\u003c\/p\u003e \u003cp\u003e12.3.3 Positron Emission Tomography (PET) 214\u003c\/p\u003e \u003cp\u003e12.3.4 Single-Photon Emission Computed Tomography (SPECT) 215\u003c\/p\u003e \u003cp\u003e12.4 Image Fusion 216\u003c\/p\u003e \u003cp\u003e12.4.1 Different Levels of Image Fusion 216\u003c\/p\u003e \u003cp\u003e12.4.1.1 Pixel Level Fusion 216\u003c\/p\u003e \u003cp\u003e12.4.1.2 Feature Level Fusion 217\u003c\/p\u003e \u003cp\u003e12.4.1.3 Decision Level Fusion 217\u003c\/p\u003e \u003cp\u003e12.5 Clinical Relevance for Medical Image Fusion 218\u003c\/p\u003e \u003cp\u003e12.5.1 Clinical Relevance for Neurocyticercosis (NCC) 218\u003c\/p\u003e \u003cp\u003e12.5.2 Clinical Relevance for Neoplastic Disease 218\u003c\/p\u003e \u003cp\u003e12.5.2.1 Clinical Relevance for Astrocytoma 218\u003c\/p\u003e \u003cp\u003e12.5.2.2 Clinical Relevance for Anaplastic Astrocytoma 219\u003c\/p\u003e \u003cp\u003e12.5.2.3 Clinical Relevance for Metastatic Bronchogenic Carcinoma 220\u003c\/p\u003e \u003cp\u003e12.5.3 Clinical Relevance for Alzheimer’s Disease 221\u003c\/p\u003e \u003cp\u003e12.6 Data Sets and Softwares Used 221\u003c\/p\u003e \u003cp\u003e12.7 Generalized Image Fusion Scheme 221\u003c\/p\u003e \u003cp\u003e12.7.1 Input Image Modalities 222\u003c\/p\u003e \u003cp\u003e12.7.2 Image Registration 222\u003c\/p\u003e \u003cp\u003e12.7.3 Fusion Process 223\u003c\/p\u003e \u003cp\u003e12.7.4 Fusion Rule 223\u003c\/p\u003e \u003cp\u003e12.7.5 Evaluation 224\u003c\/p\u003e \u003cp\u003e12.7.5.1 Subjective Evaluation 224\u003c\/p\u003e \u003cp\u003e12.7.5.2 Objective Evaluation 224\u003c\/p\u003e \u003cp\u003e12.8 Medical Image Fusion Methods 224\u003c\/p\u003e \u003cp\u003e12.8.1 Traditional Image Fusion Techniques 224\u003c\/p\u003e \u003cp\u003e12.8.1.1 Spatial Domain Image Fusion Approach 225\u003c\/p\u003e \u003cp\u003e12.8.1.2 Transform Domain Image Fusion Approach 225\u003c\/p\u003e \u003cp\u003e12.8.1.3 Fuzzy Logic–Based Image Fusion Approach 227\u003c\/p\u003e \u003cp\u003e12.8.1.4 Filtering Technique–Based Image Fusion Approach 227\u003c\/p\u003e \u003cp\u003e12.8.1.5 Neural Network–Based Image Fusion Approach 227\u003c\/p\u003e \u003cp\u003e12.8.2 Hybrid Image Fusion Techniques 228\u003c\/p\u003e \u003cp\u003e12.8.2.1 Transforms with Fuzzy Logic–Based Medical Image Fusion 228\u003c\/p\u003e \u003cp\u003e12.8.2.2 Transforms With Guided Image Filtering–Based Medical Image Fusion 229\u003c\/p\u003e \u003cp\u003e12.8.2.3 Transforms With Neural Network–Based Image Fusion 229\u003c\/p\u003e \u003cp\u003e12.9 Conclusions 233\u003c\/p\u003e \u003cp\u003e12.9.1 Future Work 234\u003c\/p\u003e \u003cp\u003eReferences 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Health Detection System for COVID-19 Patients Using IoT 237\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDipak P. Patil, Kishor Badane, Amit Kumar Mishra and Vishal A. Wankhede\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 237\u003c\/p\u003e \u003cp\u003e13.1.1 Overview 237\u003c\/p\u003e \u003cp\u003e13.1.2 Preventions 238\u003c\/p\u003e \u003cp\u003e13.1.3 Symptoms 238\u003c\/p\u003e \u003cp\u003e13.1.4 Present Situation 238\u003c\/p\u003e \u003cp\u003e13.2 Related Works 239\u003c\/p\u003e \u003cp\u003e13.3 System Design 239\u003c\/p\u003e \u003cp\u003e13.3.1 Hardware Implementation 239\u003c\/p\u003e \u003cp\u003e13.3.1.1 NodeMCU 240\u003c\/p\u003e \u003cp\u003e13.3.1.2 DHT 11 Sensor 240\u003c\/p\u003e \u003cp\u003e13.3.1.3 MAX30100 Oxygen Sensor 241\u003c\/p\u003e \u003cp\u003e13.3.1.4 ThingSpeak Server 242\u003c\/p\u003e \u003cp\u003e13.3.1.5 Arduino IDE 243\u003c\/p\u003e \u003cp\u003e13.4 Proposed System for Detection of Corona Patients 245\u003c\/p\u003e \u003cp\u003e13.4.1 Introduction 245\u003c\/p\u003e \u003cp\u003e13.4.2 Arduino IDE 246\u003c\/p\u003e \u003cp\u003e13.4.3 Hardware Implementation 246\u003c\/p\u003e \u003cp\u003e13.5 Results and Performance Analysis 247\u003c\/p\u003e \u003cp\u003e13.5.1 Hardware Implementation 247\u003c\/p\u003e \u003cp\u003e13.5.1.1 Implementation of NodeMCU With Temperature Sensor 247\u003c\/p\u003e \u003cp\u003e13.5.2 Software Implementation 248\u003c\/p\u003e \u003cp\u003e13.5.2.1 Simulation of Temperature Sensor With Arduino on Proteus Software 248\u003c\/p\u003e \u003cp\u003e13.5.2.2 Interfacing of LCD With Arduino 250\u003c\/p\u003e \u003cp\u003e13.6 Conclusion 250\u003c\/p\u003e \u003cp\u003eReferences 250\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Intelligent Systems in Healthcare 253\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRajiv Dey and Pankaj Sahu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 253\u003c\/p\u003e \u003cp\u003e14.2 Brain Computer Interface 255\u003c\/p\u003e \u003cp\u003e14.2.1 Types of Signals Used in BCI 256\u003c\/p\u003e \u003cp\u003e14.2.2 Components of BCI 257\u003c\/p\u003e \u003cp\u003e14.2.3 Applications of BCI in Health Monitoring 258\u003c\/p\u003e \u003cp\u003e14.3 Robotic Systems 258\u003c\/p\u003e \u003cp\u003e14.3.1 Advantages of Surgical Robots 258\u003c\/p\u003e \u003cp\u003e14.3.2 Centralization of the Important Information to the Surgeon 259\u003c\/p\u003e \u003cp\u003e14.3.3 Remote-Surgery, Software Development, and High Speed\u003c\/p\u003e \u003cp\u003eConnectivity Such as 5G 260\u003c\/p\u003e \u003cp\u003e14.4 Voice Recognition Systems 260\u003c\/p\u003e \u003cp\u003e14.5 Remote Health Monitoring Systems 260\u003c\/p\u003e \u003cp\u003e14.5.1 Tele-Medicine Health Concerns 262\u003c\/p\u003e \u003cp\u003e14.6 Internet of Things–Based Intelligent Systems 262\u003c\/p\u003e \u003cp\u003e14.6.1 Ubiquitous Computing Technologies in Healthcare 264\u003c\/p\u003e \u003cp\u003e14.6.2 Patient Bio-Signals and Acquisition Methods 265\u003c\/p\u003e \u003cp\u003e14.6.3 Communication Technologies Used in Healthcare Application 267\u003c\/p\u003e \u003cp\u003e14.6.4 Communication Technologies Based on Location\/Position 269\u003c\/p\u003e \u003cp\u003e14.7 Intelligent Electronic Healthcare Systems 270\u003c\/p\u003e \u003cp\u003e14.7.1 The Background of Electronic Healthcare Systems 270\u003c\/p\u003e \u003cp\u003e14.7.2 Intelligent Agents in Electronic Healthcare System 270\u003c\/p\u003e \u003cp\u003e14.7.3 Patient Data Classification Techniques 271\u003c\/p\u003e \u003cp\u003e14.8 Conclusion 271\u003c\/p\u003e \u003cp\u003eReferences 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Design of Antennas for Microwave Imaging Techniques 275\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDnyaneshwar D. Ahire, Gajanan K. Kharate and Ammar Muthana\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 275\u003c\/p\u003e \u003cp\u003e15.1.1 Overview 276\u003c\/p\u003e \u003cp\u003e15.2 Literature 277\u003c\/p\u003e \u003cp\u003e15.2.1 Microstrip Patch Antenna 278\u003c\/p\u003e \u003cp\u003e15.2.2 Early Detection of Breast Cancer and Microstrip Patch Antenna for Biomedical Application 279\u003c\/p\u003e \u003cp\u003e15.2.3 UWB for Microwave Imaging 279\u003c\/p\u003e \u003cp\u003e15.3 Design and Development of Wideband Antenna 280\u003c\/p\u003e \u003cp\u003e15.3.1 Overview 280\u003c\/p\u003e \u003cp\u003e15.3.2 Design of Rectangular Microstrip Patch Antenna 281\u003c\/p\u003e \u003cp\u003e15.3.3 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna 283\u003c\/p\u003e \u003cp\u003e15.3.4 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground 285\u003c\/p\u003e \u003cp\u003e15.3.5 Key Shape Monopole Rectangular Microstrip Patch Antenna With Rounded Corner in Partial Ground 286\u003c\/p\u003e \u003cp\u003e15.4 Results and Inferences 290\u003c\/p\u003e \u003cp\u003e15.4.1 Overview 290\u003c\/p\u003e \u003cp\u003e15.4.2 Rectangular Microstrip Patch Antenna 290\u003c\/p\u003e \u003cp\u003e15.4.2.1 Reflection and VSWR Bandwidth 290\u003c\/p\u003e \u003cp\u003e15.4.2.2 Surface Current Distribution 291\u003c\/p\u003e \u003cp\u003e15.4.3 Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground 292\u003c\/p\u003e \u003cp\u003e15.4.3.1 Reflection and VSWR Bandwidth 292\u003c\/p\u003e \u003cp\u003e15.4.3.2 Surface Current Distribution 292\u003c\/p\u003e \u003cp\u003e15.4.3.3 Inference 293\u003c\/p\u003e \u003cp\u003e15.4.4 Key Shape Monopole Rectangular Microstrip Patch Antenna with Rounded Corner in Partial Ground 294\u003c\/p\u003e \u003cp\u003e15.4.4.1 Reflection and VSWR Bandwidth 294\u003c\/p\u003e \u003cp\u003e15.4.4.2 Surface Current Distribution 294\u003c\/p\u003e \u003cp\u003e15.4.4.3 Results of the Fabricated Antenna 295\u003c\/p\u003e \u003cp\u003e15.4.4.4 Inference 296\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 297\u003c\/p\u003e \u003cp\u003eReferences 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 COVID-19: A Global Crisis 303\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSavita Mandan and Durgeshwari Kalal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 303\u003c\/p\u003e \u003cp\u003e16.1.1 Structure 304\u003c\/p\u003e \u003cp\u003e16.1.2 Classification of Corona Virus 304\u003c\/p\u003e \u003cp\u003e16.1.3 Types of Human Coronavirus 304\u003c\/p\u003e \u003cp\u003e16.1.4 Genome Organization of Corona Virus 305\u003c\/p\u003e \u003cp\u003e16.1.5 Coronavirus Replication 305\u003c\/p\u003e \u003cp\u003e16.1.6 Host Defenses 306\u003c\/p\u003e \u003cp\u003e16.2 Clinical Manifestation and Pathogenesis 306\u003c\/p\u003e \u003cp\u003e16.2.1 Symptoms 307\u003c\/p\u003e \u003cp\u003e16.2.2 Epidemiology 307\u003c\/p\u003e \u003cp\u003e16.3 Diagnosis and Control 308\u003c\/p\u003e \u003cp\u003e16.3.1 Molecular Test 308\u003c\/p\u003e \u003cp\u003e16.3.2 Serology 308\u003c\/p\u003e \u003cp\u003e16.3.3 Concerning Lab Assessments 309\u003c\/p\u003e \u003cp\u003e16.3.4 Significantly Improved D-Dimer 309\u003c\/p\u003e \u003cp\u003e16.3.5 Imaging 309\u003c\/p\u003e \u003cp\u003e16.3.6 HRCT 309\u003c\/p\u003e \u003cp\u003e16.3.7 Lung Ultrasound 310\u003c\/p\u003e \u003cp\u003e16.4 Control Measures 310\u003c\/p\u003e \u003cp\u003e16.4.1 Prevention and Patient Education 311\u003c\/p\u003e \u003cp\u003e16.5 Immunization 312\u003c\/p\u003e \u003cp\u003e16.5.1 Medications 312\u003c\/p\u003e \u003cp\u003e16.6 Conclusion 313\u003c\/p\u003e \u003cp\u003eReferences 313\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Smart Healthcare for Pregnant Women in Rural Areas 317\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eD. Shanthi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 317\u003c\/p\u003e \u003cp\u003e17.2 National\/International Surveys Reviews 319\u003c\/p\u003e \u003cp\u003e17.2.1 National Family Health Survey Review-11 319\u003c\/p\u003e \u003cp\u003e17.2.2 National Family Health Survey Review-2.2 319\u003c\/p\u003e \u003cp\u003e17.2.3 National Family Health Survey Reviews-3 320\u003c\/p\u003e \u003cp\u003e17.3 Architecture 320\u003c\/p\u003e \u003cp\u003e17.4 Anganwadi’s Collaborative Work 321\u003c\/p\u003e \u003cp\u003e17.5 Schemes Offered by Central\/State Governments 321\u003c\/p\u003e \u003cp\u003e17.5.1 AAH (Anna Amrutha Hastham) 321\u003c\/p\u003e \u003cp\u003e17.5.2 Programme Arogya Laxmi 323\u003c\/p\u003e \u003cp\u003e17.5.3 Balamrutham-Kids’ Weaning Food from 7 Months to 3 Years 323\u003c\/p\u003e \u003cp\u003e17.5.4 Nutri TASC (Tracking of Group Responsibility for Services) 323\u003c\/p\u003e \u003cp\u003e17.5.5 Akshyapatra Foundation (ISKCON) 324\u003c\/p\u003e \u003cp\u003e17.5.6 Mahila Sishu Chaitanyam 324\u003c\/p\u003e \u003cp\u003e17.5.7 Community Management of Acute Malnutrition 325\u003c\/p\u003e \u003cp\u003e17.5.8 Child Health Nutrition Committee 325\u003c\/p\u003e \u003cp\u003e17.5.9 Bharat Ratna APJ Abdul Kalam Amrut Yojna 325\u003c\/p\u003e \u003cp\u003e17.6 Smart Healthcare System 326\u003c\/p\u003e \u003cp\u003e17.7 Data Collection 328\u003c\/p\u003e \u003cp\u003e17.8 Hardware and Software Features of HCS 328\u003c\/p\u003e \u003cp\u003e17.9 Implementation 329\u003c\/p\u003e \u003cp\u003e17.9.1 Modules 329\u003c\/p\u003e \u003cp\u003e17.9.2 Modules Description 329\u003c\/p\u003e \u003cp\u003e17.9.2.1 Data Preprocessing 329\u003c\/p\u003e \u003cp\u003e17.9.2.2 Component Features Extraction 329\u003c\/p\u003e \u003cp\u003e17.9.2.3 User Sentimental Measurement 330\u003c\/p\u003e \u003cp\u003e17.9.2.4 Sentiment Evaluation 330\u003c\/p\u003e \u003cp\u003e17.10 Results and Analysis 331\u003c\/p\u003e \u003cp\u003e17.11 Conclusion 333\u003c\/p\u003e \u003cp\u003eReferences 333\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Computer-Aided Interpretation of ECG Signal—A Challenge 335\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShalini Sahay and A.K. Wadhwani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 336\u003c\/p\u003e \u003cp\u003e18.1.1 Electrical Activity of the Heart 336\u003c\/p\u003e \u003cp\u003e18.2 The Cardiovascular System 338\u003c\/p\u003e \u003cp\u003e18.3 Electrocardiogram Leads 340\u003c\/p\u003e \u003cp\u003e18.4 Artifacts\/Noises Affecting the ECG 342\u003c\/p\u003e \u003cp\u003e18.4.1 Baseline Wander 343\u003c\/p\u003e \u003cp\u003e18.4.2 Power Line Interference 343\u003c\/p\u003e \u003cp\u003e18.4.3 Motion Artifacts 344\u003c\/p\u003e \u003cp\u003e18.4.4 Muscle Noise 344\u003c\/p\u003e \u003cp\u003e18.4.5 Instrumentation Noise 344\u003c\/p\u003e \u003cp\u003e18.4.6 Other Interferences 345\u003c\/p\u003e \u003cp\u003e18.5 The ECG Waveform 346\u003c\/p\u003e \u003cp\u003e18.5.1 Normal Sinus Rhythm 347\u003c\/p\u003e \u003cp\u003e18.6 Cardiac Arrhythmias 347\u003c\/p\u003e \u003cp\u003e18.6.1 Sinus Bradycardia 347\u003c\/p\u003e \u003cp\u003e18.6.2 Sinus Tachycardia 348\u003c\/p\u003e \u003cp\u003e18.6.3 Atrial Flutter 348\u003c\/p\u003e \u003cp\u003e18.6.4 Atrial Fibrillation 349\u003c\/p\u003e \u003cp\u003e18.6.5 Ventric ular Tachycardia 349\u003c\/p\u003e \u003cp\u003e18.6.6 AV Block 2 First Degree 350\u003c\/p\u003e \u003cp\u003e18.6.7 Asystole 350\u003c\/p\u003e \u003cp\u003e18.7 Electrocardiogram Databases 351\u003c\/p\u003e \u003cp\u003e18.8 Computer-Aided Interpretation (CAD) 351\u003c\/p\u003e \u003cp\u003e18.9 Computational Techniques 354\u003c\/p\u003e \u003cp\u003e18.10 Conclusion 356\u003c\/p\u003e \u003cp\u003eReferences 357\u003c\/p\u003e \u003cp\u003eIndex 359\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430972846360,"sku":"9781119819134","price":169.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119819134.jpg?v=1784766837","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/medical-imaging-and-health-informatics-hardback-9781119819134","provider":"Freshly Printed Books","version":"1.0","type":"link"}