This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world. Medical imaging offers essential information on patients’ medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an “information overload” for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment. Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field.

SMART SYSTEMS FOR INDUSTRIAL APPLICATIONS The prime objective of this book is to provide an insight into the role and advancements of artificial intelligence in electrical systems and future challenges. The book covers a broad range of topics about AI from a multidisciplinary point of view, starting with its history and continuing on to theories about artificial vs. human intelligence, concepts, and regulations concerning AI, human-machine distribution of power and control, delegation of decisions, the social and economic impact of AI, etc. The prominent role that AI plays in society by connecting people through technologies is highlighted in this book. It also covers key aspects of various AI applications in electrical systems in order to enable growth in electrical engineering. The impact that AI has on social and economic factors is also examined from various perspectives. Moreover, many intriguing aspects of AI techniques in different domains are covered such as e-learning, healthcare, smart grid, virtual assistance, etc. Audience The book will be of interest to researchers and postgraduate students in artificial intelligence, electrical and electronic engineering, as well as those engineers working in the application areas such as healthcare, energy systems, education, and others.

The book explains to physicians and technologists the value and limitations of artificial intelligence in the management of disease. Specifically, it explains how machine learning and new types of data analysis will improve diagnosis and personalize patient care.

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Applications of Artificial Intelligence in Medical Imaging provides the description of various biomedical image analysis in disease detection using AI that can be used to incorporate knowledge obtained from different medical imaging devices such as CT, X-ray, PET and ultrasound. The book discusses the use of AI for detection of several cancer types, including brain tumor, breast, pancreatic, rectal, lung colon, and skin. In addition, it explains how AI and deep learning techniques can be used to diagnose Alzheimer's, Parkinson's, COVID-19 and mental conditions. This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about AI and its impact in medical/biomedical image analysis. Discusses new deep learning algorithms for image analysis and how they are used for medical images Provides several examples for each imaging technique, along with their application areas so that readers can rely on them as a clinical decision support system Describes how new AI tools may contribute significantly to the successful enhancement of a single patient's clinical knowledge to improve treatment outcomes

Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided diagnosis systems for neurological disorders. While computer-aided decision support systems for different medical imaging modalities are available, this is the first book to solely concentrate on decision support systems for neurological disorders. Due to the increase in the prevalence of diseases such as Alzheimer, Parkinson’s and Dementia, this book will have significant importance in the medical field. Topics discussed include recent computational approaches, different types of neurological disorders, deep convolution neural networks, generative adversarial networks, auto encoders, recurrent neural networks, and modified/hybrid artificial neural networks. Includes applications of computer intelligence and decision support systems for the diagnosis and analysis of a variety of neurological disorders Presents in-depth, technical coverage of computer-aided systems for tumor image classification, Alzheimer’s disease detection, dementia detection using deep belief neural networks, and morphological approaches for stroke detection Covers disease diagnosis for cerebral palsy using auto-encoder approaches, contrast enhancement for performance enhanced diagnosis systems, autism detection using fuzzy logic systems, and autism detection using generative adversarial networks Written by engineers to help engineers, computer scientists, researchers and clinicians understand the technology and applications of decision support systems for neurological disorders

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.