Medical Imaging and Health Informatics

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Alzheimer's disease
Anaplastic Astrocytoma
Arrhythmia
Artificial Neural Network
Astrocytoma
Attention Network
Bayesian Regularization
Biosignals
Bone Fracture
Bone Mineral Density
Breast cancer
Cardiac Diseases
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Computational Techniques
Computer Aided Diagnosis
Computer Tomograpy
Congestive Heart Failure
Convolutional Neural Network
COVID-19
Crack Detection
CT Scan
Deep Learning
Dengue Prediction
ECG
Edge Detection
Embolization
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Feature Fusion
Feature Recalibration
Gradient Harmony Search
Health Care
Hemagimas
Image Processing
Interoperator
IoT
LDA
LeNet Architecture
Levenberg-Marquardt
Liver Segmentation
Liver Tumor
Lungs infection
Machine learning
Malaria Parasite
Malignant
Mammogram
Metastatic Brochogenic Carcinoma
Microscopic image segmentation
Morphometric Analysis
MRI
Multi-Scale features
Naive Bayes classifier
Neural Network
Neurocysticercosis
Node MCU
Noise Removal
Non-linear Auto-Regressive
Osteoporosis
Otsu Algorithm
Patch Antenna
PET
Plasmodium
Pneumonia Detection
Post-Menopausal Women
Predictive Model
RBC
RBFN
Region Growing
Regression
Reinforcement Learning
Scaled Conjugate Gradient
Segmentation
Sensor
SPECT
Statistical Analysis
Supervised Learning
SVM
Thresholding
Time Series Model
Ultrasound Images
Unsupervised Learning
X-ray

Product details

  • ISBN 9781119819134
  • Weight: 454g
  • Dimensions: 10 x 10mm
  • Publication Date: 29 Aug 2022
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.

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.