Medical Imaging and Health Informatics

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

• Publisher: Wiley-Scrivener
• Year: 2022
• Print ISBN: 9781119819134
• eBook ISBN: 9781119819141
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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.

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Preface
• 1 Machine Learning Approach for Medical Diagnosis Based on Prediction Model
• 2 Applications of Machine Learning Techniques in Disease Detection
• 3 Dengue Incidence Rate Prediction Using Nonlinear Autoregressive Neural Network Time Series Model
• 4 Early Detection of Breast Cancer Using Machine Learning
• 5 Machine Learning Approach for Prediction of Lung Cancer
• 6 Segmentation of Liver Tumor Using ANN
• 7 DMSAN: Deep Multi-Scale Attention Network for Automatic Liver Segmentation From Abdomen CT Images
• 8 AI-Based Identification and Prediction of Cardiac Disorders
• 9 An Implementation of Image Processing Technique for Bone Fracture Detection Including Classification
• 10 Improved Otsu Algorithm for Segmentation of Malaria Parasite Images
• 11 A Reliable and Fully Automated Diagnosis of COVID-19 Based on Computed Tomography
• 12 Multimodality Medical Images for Healthcare Disease Analysis
• 13 Health Detection System for COVID-19 Patients Using IoT
• 14 Intelligent Systems in Healthcare
• 15 Design of Antennas for Microwave Imaging Techniques
• 16 COVID-19: A Global Crisis
• 17 Smart Healthcare for Pregnant Women in Rural Areas
• 18 Computer-Aided Interpretation of ECG Signal—A Challenge
• Index
• Also of Interest
• Wiley End User License Agreement