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.
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