Medical Imaging and Health Informatics (eBook, ePUB)

Redaktion: Jaware, Tushar H.; Antonov, Svetlin; Badgujar, Ravindra D.; Kumar, K. Sarat

• Format: ePub

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Wiley
• Seitenzahl: 384
• Erscheinungstermin: 26. Mai 2022
• Englisch
• ISBN-13: 9781119819141
• Artikelnr.: 64167977

Autorenporträt

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.

Inhaltsangabe

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
&