Smart Healthcare for Disease Diagnosis and Prevention focuses on the advancement in healthcare technology to improve human health at all levels using smart technologies. It covers all necessary topics from basic concepts (such as signal and image processing) to advanced knowledge on topics such as tissue engineering, virtual and intelligent instrumentation (or VLSI) and Embedded Systems. This book can be used to guide students and young researchers, providing basic knowledge on signal/image processing and smart technologies. Users will find a perfect blend of the interdisciplinary approach to biomedical engineering.

The book considers many technical concepts, emerging technologies, real-world healthcare applications, and many other technical, multidisciplinary notions in the same content. Finally, it systemically introduces the technologies and devices for healthcare objects and targets disease diagnosis and prevention in different views.

Discusses how new advanced technologies are used in real healthcare applications to improve patient safety Explores how medical data such as signals and images can be used in diagnosis Covers how wireless communications devices, such as sensor networks, RFID, wireless body area network, and wearable sensors are used in the medical environment

Smart Healthcare for Disease Diagnosis and Prevention focuses on the advancement in healthcare technology to improve human health at all levels using smart technologies. It covers all necessary topics from basic concepts (such as signal and image processing) to advanced knowledge on topics such as tissue engineering, virtual and intelligent instrumentation (or VLSI) and Embedded Systems. This book can be used to guide students and young researchers, providing basic knowledge on signal/image processing and smart technologies. Users will find a perfect blend of the interdisciplinary approach to biomedical engineering.The book considers many technical concepts, emerging technologies, real-world healthcare applications, and many other technical, multidisciplinary notions in the same content. Finally, it systemically introduces the technologies and devices for healthcare objects and targets disease diagnosis and prevention in different views.- Discusses how new advanced technologies are used in real healthcare applications to improve patient safety- Explores how medical data such as signals and images can be used in diagnosis- Covers how wireless communications devices, such as sensor networks, RFID, wireless body area network, and wearable sensors are used in the medical environment

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they're flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare. Provides coverage of fuzzy logic, neural networks, evolutionary computation, learning theory, probabilistic methods, telemedicine, and robotics applications Includes coverage of artificial intelligence and biological applications, soft computing, image and signal processing, and genetic algorithms Presents the latest developments in computational methods in healthcare Bridges the gap between obsolete literature and current literature

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they''re flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare.- Provides coverage of fuzzy logic, neural networks, evolutionary computation, learning theory, probabilistic methods, telemedicine, and robotics applications- Includes coverage of artificial intelligence and biological applications, soft computing, image and signal processing, and genetic algorithms- Presents the latest developments in computational methods in healthcare- Bridges the gap between obsolete literature and current literature

Introduction to Biomedical Instrumentation and Its Applications delivers a detailed overview of the various instruments used in the biomedical and healthcare domain, focusing on both their main features and their uses in the medical industry. Each chapter focuses on biomedical instrumentation in a different medical discipline, covering a range of different topics including radiological devices, instruments used for blood analysis, defibrillators, ventilators, nerve stimulators and baby incubators. This book seeks to provide the reader with in-depth knowledge on biomedical devices, thus enabling them to contribute to the future development of instruments in the healthcare domain.

This is a concise handbook that will be useful to students, researchers and practitioners involved in biomedical engineering, as well as doctors and clinicians who specialize in areas such as cardiology, anesthesiology and physiotherapy.

Provides detailed insights into a variety of biomedical instruments for use in different medical areas such as radiology, cardiology and physiotherapy Considers the advantages, disadvantages and future developments of various biomedical instruments Equips researchers with an understanding of the working principles of various instruments, thus preparing them for the future development and design of innovative devices in the health domain Contains various mathematical derivations and numerical data that connect theory with the practical environment Features a section on patient safety and infection control in relation to the use of biomedical instruments

Introduction to Biomedical Instrumentation and Its Applications delivers a detailed overview of the various instruments used in the biomedical and healthcare domain, focusing on both their main features and their uses in the medical industry. Each chapter focuses on biomedical instrumentation in a different medical discipline, covering a range of different topics including radiological devices, instruments used for blood analysis, defibrillators, ventilators, nerve stimulators and baby incubators. This book seeks to provide the reader with in-depth knowledge on biomedical devices, thus enabling them to contribute to the future development of instruments in the healthcare domain.This is a concise handbook that will be useful to students, researchers and practitioners involved in biomedical engineering, as well as doctors and clinicians who specialize in areas such as cardiology, anesthesiology and physiotherapy.- Provides detailed insights into a variety of biomedical instruments for use in different medical areas such as radiology, cardiology and physiotherapy- Considers the advantages, disadvantages and future developments of various biomedical instruments- Equips researchers with an understanding of the working principles of various instruments, thus preparing them for the future development and design of innovative devices in the health domain- Contains various mathematical derivations and numerical data that connect theory with the practical environment- Features a section on patient safety and infection control in relation to the use of biomedical instruments

Artificial Intelligence in e-health Framework, Volume One: AI, Classification, Wearable Devices, and Computer-Aided Diagnosis presents a variety of AI techniques and applications for solving issues in the healthcare industry. As Artificial Intelligence is increasingly incorporated into medical systems and methods, it is critical to understand the formulations and basics of machine and deep learning as well as how to implement these advances into practice. This book specifically explores Artificial Intelligence developments in disease diagnosis, health monitoring, medical image recognition, and diagnostics, as well as e-health records management.This is a valuable resource for health professionals, scientists, researchers, students, and all who wish to broaden their knowledge in this advancing technology.

Provides an in-depth introduction to Artificial Intelligence in e-health frameworkReviews theoretical and application information to develop understanding of AI advances in diagnostics, health monitoring, and records managementDiscusses advanced AI techniques in both machine learning and deep learning for solving healthcare industry issues

Artificial Intelligence in e-health Framework, Volume One: AI, Classification, Wearable Devices, and Computer-Aided Diagnosis presents a variety of AI techniques and applications for solving issues in the healthcare industry. As Artificial Intelligence is increasingly incorporated into medical systems and methods, it is critical to understand the formulations and basics of machine and deep learning as well as how to implement these advances into practice. This book specifically explores Artificial Intelligence developments in disease diagnosis, health monitoring, medical image recognition, and diagnostics, as well as e-health records management.This is a valuable resource for health professionals, scientists, researchers, students, and all who wish to broaden their knowledge in this advancing technology.- Provides an in-depth introduction to Artificial Intelligence in e-health framework- Reviews theoretical and application information to develop understanding of AI advances in diagnostics, health monitoring, and records management- Discusses advanced AI techniques in both machine learning and deep learning for solving healthcare industry issues

Artificial Intelligence in e-health Framework, Volume Two: Telemedicine Applications, Drug Delivery, Augmented Reality, and Robotics offers a comprehensive exploration of AI's role in enhancing medical solutions. Through a focus on machine learning, deep learning, and their practical applications, this book aims to bridge the gap between theoretical advancements and real-world healthcare implementations. In addition to telemedicine advancements, the book explores AI-driven drug discovery, virtual and augmented reality innovations, robotics in medicine, and radiotherapy applications. It serves as an indispensable resource for professionals, researchers, and students eager to deepen their understanding of AI's contributions to modern healthcare. - Provides an in-depth introduction to Artificial Intelligence in an e-health framework- Reviews theoretical and application information to develop our understanding of AI advances in telemedicine, drug development, virtual and augmented reality, robotics, and radiotherapy applications- Discusses advanced AI techniques in both treatment planning and AI-based quality assurance for solving healthcare industry issues

Artificial Intelligence in e-health Framework, Volume Two: Telemedicine Applications, Drug Delivery, Augmented Reality, and Robotics offers a comprehensive exploration of AI’s role in enhancing medical solutions. Through a focus on machine learning, deep learning, and their practical applications, this book aims to bridge the gap between theoretical advancements and real-world healthcare implementations. In addition to telemedicine advancements, the book explores AI-driven drug discovery, virtual and augmented reality innovations, robotics in medicine, and radiotherapy applications. It serves as an indispensable resource for professionals, researchers, and students eager to deepen their understanding of AI's contributions to modern healthcare.

Provides an in-depth introduction to Artificial Intelligence in an e-health frameworkReviews theoretical and application information to develop our understanding of AI advances in telemedicine, drug development, virtual and augmented reality, robotics, and radiotherapy applicationsDiscusses advanced AI techniques in both treatment planning and AI-based quality assurance for solving healthcare industry issues

The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results.

With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques.

Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented.

Key features:

Provides descriptions of different medical imaging modalities and their applications Discusses the basics and advanced aspects of parallel computing with different multicore architectures Expounds on the need for embedding data and task parallelism in different medical image processing techniques Presents helpful examples and case studies of the discussed methods

This book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.

The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results.

With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques.

Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented.

Key features:

Provides descriptions of different medical imaging modalities and their applications Discusses the basics and advanced aspects of parallel computing with different multicore architectures Expounds on the need for embedding data and task parallelism in different medical image processing techniques Presents helpful examples and case studies of the discussed methods

This book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.

Assistive Technology Intervention in Healthcare focuses on various applications of intelligent techniques in biomedical engineering and health informatics. It aims to create awareness about disability reduction and recovery of accidental disability with the help of various rehabilitative systems. Novel technologies in disability treatment, management and assistance, including healthcare devices and their utility from home to hospital, are described. The book deals with simulation, modeling, measurement, control, analysis, information extraction and monitoring of physiological data in clinical medicine and biology.

Features

Covers the latest evolutionary approaches to solve optimization problems in the biomedical engineering field

Explains machine learning–based approaches to improvement in health engineering areas

Reviews the IoT, cloud computing and data analytics in healthcare informatics

Discusses modeling and simulations in the design of biomedical equipment

Explores monitoring of physiological data

This book is aimed at researchers and graduate students in biomedical engineering, clinical engineering and bioinformatics.

Assistive Technology Intervention in Healthcare focuses on various applications of intelligent techniques in biomedical engineering and health informatics. It aims to create awareness about disability reduction and recovery of accidental disability with the help of various rehabilitative systems. Novel technologies in disability treatment, management and assistance, including healthcare devices and their utility from home to hospital, are described. The book deals with simulation, modeling, measurement, control, analysis, information extraction and monitoring of physiological data in clinical medicine and biology.

Features

Covers the latest evolutionary approaches to solve optimization problems in the biomedical engineering field

Explains machine learning–based approaches to improvement in health engineering areas

Reviews the IoT, cloud computing and data analytics in healthcare informatics

Discusses modeling and simulations in the design of biomedical equipment

Explores monitoring of physiological data

This book is aimed at researchers and graduate students in biomedical engineering, clinical engineering and bioinformatics.

Assistive Technology Intervention in Healthcare focuses on various applications of intelligent techniques in biomedical engineering and health informatics. It aims to create awareness about disability reduction and recovery of accidental disability with the help of various rehabilitative systems. Novel technologies in disability treatment, management and assistance, including healthcare devices and their utility from home to hospital, are described. The book deals with simulation, modeling, measurement, control, analysis, information extraction and monitoring of physiological data in clinical medicine and biology. Features Covers the latest evolutionary approaches to solve optimization problems in the biomedical engineering field Explains machine learning–based approaches to improvement in health engineering areas Reviews the IoT, cloud computing and data analytics in healthcare informatics Discusses modeling and simulations in the design of biomedical equipment Explores monitoring of physiological dataThis book is aimed at researchers and graduate students in biomedical engineering, clinical engineering and bioinformatics.

Assistive Technology Intervention in Healthcare focuses on various applications of intelligent techniques in biomedical engineering and health informatics. It aims to create awareness about disability reduction and recovery of accidental disability with the help of various rehabilitative systems. Novel technologies in disability treatment, management and assistance, including healthcare devices and their utility from home to hospital, are described. The book deals with simulation, modeling, measurement, control, analysis, information extraction and monitoring of physiological data in clinical medicine and biology. Features Covers the latest evolutionary approaches to solve optimization problems in the biomedical engineering field Explains machine learning–based approaches to improvement in health engineering areas Reviews the IoT, cloud computing and data analytics in healthcare informatics Discusses modeling and simulations in the design of biomedical equipment Explores monitoring of physiological dataThis book is aimed at researchers and graduate students in biomedical engineering, clinical engineering and bioinformatics.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

This book presents the latest technological advancements in the understanding and diagnosis of neurological disorders. The chapters provide the complexities of diagnosis, understanding pathogenesis, and unveiling the latest therapeutic strategies against neurodegenerative disorders. Innovative Neurodiagnostics and Therapeutics: Understanding and Advancing Treatment for Neurological Disorders also explores the transformative role of optogenetics, nanoarchitectonics, machine learning, and artificial intelligence in neurodiagnosis. Metal organic frameworks are explored for their prospective applications in neuroscience. The book continues to unfold with a focus on robotic advancements in neurotherapeutics, offering comprehensive insights into the cutting edge of neurotherapeutic interventions. The complex interplay between neurodiagnostics and psychodiagnostics is explored in the context of substance use disorders. The book concludes by examining the potential of aquaporin as a therapeutic target in neurological disorders. As such, this book is an invaluable source for neuroscientists, neurologists, and computational biologists.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

This book presents the latest technological advancements in the understanding and diagnosis of neurological disorders. The chapters provide the complexities of diagnosis, understanding pathogenesis, and unveiling the latest therapeutic strategies against neurodegenerative disorders. The book also explores the transformative role of optogenetics, nanoarchitectonics, machine learning, and artificial intelligence in neurodiagnosis. Metal organic frameworks are explored for their prospective applications in neuroscience. The book continues to unfold with a focus on robotic advancements in neurotherapeutics, offering comprehensive insights into the cutting edge of neurotherapeutic interventions. The complex interplay between neurodiagnostics and psychodiagnostics is explored in the context of substance use disorders. The book concludes by examining the potential of aquaporin as a therapeutic target in neurological disorders. As such, this book is an invaluable source for neuroscientists, neurologists, and computational biologists.

Key Features

Integrates insights from neuroscience, technology, and computational biology to present a holistic understanding of neurological disorders. Explores complexities of diagnosis, pathogenesis, and therapeutic strategies for neurological disorders. Highlights the latest advancements, including optogenetics, nanoarchitectonics, and machine learning for neurodiagnosis. Offers a detailed exploration of robotic advancements in neurotherapeutics. Addresses the complex interplay between neurodiagnostics and psychodiagnostics in substance use disorders.

This book presents the latest technological advancements in the understanding and diagnosis of neurological disorders. The chapters provide the complexities of diagnosis, understanding pathogenesis, and unveiling the latest therapeutic strategies against neurodegenerative disorders. The book also explores the transformative role of optogenetics, nanoarchitectonics, machine learning, and artificial intelligence in neurodiagnosis. Metal organic frameworks are explored for their prospective applications in neuroscience. The book continues to unfold with a focus on robotic advancements in neurotherapeutics, offering comprehensive insights into the cutting edge of neurotherapeutic interventions. The complex interplay between neurodiagnostics and psychodiagnostics is explored in the context of substance use disorders. The book concludes by examining the potential of aquaporin as a therapeutic target in neurological disorders. As such, this book is an invaluable source for neuroscientists, neurologists, and computational biologists.

Key Features

Integrates insights from neuroscience, technology, and computational biology to present a holistic understanding of neurological disorders. Explores complexities of diagnosis, pathogenesis, and therapeutic strategies for neurological disorders. Highlights the latest advancements, including optogenetics, nanoarchitectonics, and machine learning for neurodiagnosis. Offers a detailed exploration of robotic advancements in neurotherapeutics. Addresses the complex interplay between neurodiagnostics and psychodiagnostics in substance use disorders.

The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results.With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques.Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented.Key features:Provides descriptions of different medical imaging modalities and their applicationsDiscusses the basics and advanced aspects of parallel computing with different multicore architecturesExpounds on the need for embedding data and task parallelism in different medical image processing techniquesPresents helpful examples and case studies of the discussed methodsThis book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.

The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results.With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques.Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented.Key features:Provides descriptions of different medical imaging modalities and their applicationsDiscusses the basics and advanced aspects of parallel computing with different multicore architecturesExpounds on the need for embedding data and task parallelism in different medical image processing techniquesPresents helpful examples and case studies of the discussed methodsThis book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.

Artificial intelligent systems, which offer great improvement in healthcare sector assisted by machine learning, wireless communications, data analytics, cognitive computing, and mobile computing provide more intelligent and convenient solutions and services. With the help of the advanced techniques, now a days it is possible to understand human body and to handle & process the health data anytime and anywhere. It is a smart healthcare system which includes patient, hospital management, doctors, monitoring, diagnosis, decision making modules, disease prevention to meet the challenges and problems arises in healthcare industry. Furthermore, the advanced healthcare systems need to upgrade with new capabilities to provide human with more intelligent and professional healthcare services to further improve the quality of service and user experience. To explore recent advances and disseminate state-of-the-art techniques related to intelligent healthcare services andapplications. This edited book involved in designing systems that will permit the societal acceptance of ambient intelligence including signal processing, imaging, computing, instrumentation, artificial intelligence, internet of health things, data analytics, disease detection, telemedicine, and their applications. As the book includes recent trends in research issues and applications, the contents will be beneficial to Professors, researchers, and engineers. This book will provide support and aid to the researchers involved in designing latest advancements in communication and intelligent systems that will permit the societal acceptance of ambient intelligence. This book presents the latest research being conducted on diverse topics in intelligence technologies with the goal of advancing knowledge and applications healthcare sector and to present the latest snapshot of the ongoing research as well as to shed further light on future directions in this space. The aim of publishing the book is to serve for educators, researchers, and developers working in recent advances and upcoming technologies utilizing computational sciences.

Artificial intelligent systems, which offer great improvement in healthcare sector assisted by machine learning, wireless communications, data analytics, cognitive computing, and mobile computing provide more intelligent and convenient solutions and services. With the help of the advanced techniques, now a days it is possible to understand human body and to handle & process the health data anytime and anywhere. It is a smart healthcare system which includes patient, hospital management, doctors, monitoring, diagnosis, decision making modules, disease prevention to meet the challenges and problems arises in healthcare industry. Furthermore, the advanced healthcare systems need to upgrade with new capabilities to provide human with more intelligent and professional healthcare services to further improve the quality of service and user experience. To explore recent advances and disseminate state-of-the-art techniques related to intelligent healthcare services andapplications. This edited book involved in designing systems that will permit the societal acceptance of ambient intelligence including signal processing, imaging, computing, instrumentation, artificial intelligence, internet of health things, data analytics, disease detection, telemedicine, and their applications. As the book includes recent trends in research issues and applications, the contents will be beneficial to Professors, researchers, and engineers. This book will provide support and aid to the researchers involved in designing latest advancements in communication and intelligent systems that will permit the societal acceptance of ambient intelligence. This book presents the latest research being conducted on diverse topics in intelligence technologies with the goal of advancing knowledge and applications healthcare sector and to present the latest snapshot of the ongoing research as well as to shed further light on future directions in this space. The aim of publishing the book is to serve for educators, researchers, and developers working in recent advances and upcoming technologies utilizing computational sciences.

Smart Healthcare for Disease Diagnosis and Prevention focuses on the advancement in healthcare technology to improve human health at all levels using smart technologies. It covers all necessary topics from basic concepts (such as signal and image processing) to advanced knowledge on topics such as tissue engineering, virtual and intelligent instrumentation (or VLSI) and Embedded Systems. This book can be used to guide students and young researchers, providing basic knowledge on signal/image processing and smart technologies. Users will find a perfect blend of the interdisciplinary approach to biomedical engineering.

The book considers many technical concepts, emerging technologies, real-world healthcare applications, and many other technical, multidisciplinary notions in the same content. Finally, it systemically introduces the technologies and devices for healthcare objects and targets disease diagnosis and prevention in different views.

Discusses how new advanced technologies are used in real healthcare applications to improve patient safety Explores how medical data such as signals and images can be used in diagnosis Covers how wireless communications devices, such as sensor networks, RFID, wireless body area network, and wearable sensors are used in the medical environment

Smart Healthcare for Disease Diagnosis and Prevention focuses on the advancement in healthcare technology to improve human health at all levels using smart technologies. It covers all necessary topics from basic concepts (such as signal and image processing) to advanced knowledge on topics such as tissue engineering, virtual and intelligent instrumentation (or VLSI) and Embedded Systems. This book can be used to guide students and young researchers, providing basic knowledge on signal/image processing and smart technologies. Users will find a perfect blend of the interdisciplinary approach to biomedical engineering.The book considers many technical concepts, emerging technologies, real-world healthcare applications, and many other technical, multidisciplinary notions in the same content. Finally, it systemically introduces the technologies and devices for healthcare objects and targets disease diagnosis and prevention in different views.- Discusses how new advanced technologies are used in real healthcare applications to improve patient safety- Explores how medical data such as signals and images can be used in diagnosis- Covers how wireless communications devices, such as sensor networks, RFID, wireless body area network, and wearable sensors are used in the medical environment

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they're flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare. Provides coverage of fuzzy logic, neural networks, evolutionary computation, learning theory, probabilistic methods, telemedicine, and robotics applications Includes coverage of artificial intelligence and biological applications, soft computing, image and signal processing, and genetic algorithms Presents the latest developments in computational methods in healthcare Bridges the gap between obsolete literature and current literature

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they''re flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare.- Provides coverage of fuzzy logic, neural networks, evolutionary computation, learning theory, probabilistic methods, telemedicine, and robotics applications- Includes coverage of artificial intelligence and biological applications, soft computing, image and signal processing, and genetic algorithms- Presents the latest developments in computational methods in healthcare- Bridges the gap between obsolete literature and current literature

Introduction to Biomedical Instrumentation and Its Applications delivers a detailed overview of the various instruments used in the biomedical and healthcare domain, focusing on both their main features and their uses in the medical industry. Each chapter focuses on biomedical instrumentation in a different medical discipline, covering a range of different topics including radiological devices, instruments used for blood analysis, defibrillators, ventilators, nerve stimulators and baby incubators. This book seeks to provide the reader with in-depth knowledge on biomedical devices, thus enabling them to contribute to the future development of instruments in the healthcare domain.

This is a concise handbook that will be useful to students, researchers and practitioners involved in biomedical engineering, as well as doctors and clinicians who specialize in areas such as cardiology, anesthesiology and physiotherapy.

Provides detailed insights into a variety of biomedical instruments for use in different medical areas such as radiology, cardiology and physiotherapy Considers the advantages, disadvantages and future developments of various biomedical instruments Equips researchers with an understanding of the working principles of various instruments, thus preparing them for the future development and design of innovative devices in the health domain Contains various mathematical derivations and numerical data that connect theory with the practical environment Features a section on patient safety and infection control in relation to the use of biomedical instruments

Introduction to Biomedical Instrumentation and Its Applications delivers a detailed overview of the various instruments used in the biomedical and healthcare domain, focusing on both their main features and their uses in the medical industry. Each chapter focuses on biomedical instrumentation in a different medical discipline, covering a range of different topics including radiological devices, instruments used for blood analysis, defibrillators, ventilators, nerve stimulators and baby incubators. This book seeks to provide the reader with in-depth knowledge on biomedical devices, thus enabling them to contribute to the future development of instruments in the healthcare domain.This is a concise handbook that will be useful to students, researchers and practitioners involved in biomedical engineering, as well as doctors and clinicians who specialize in areas such as cardiology, anesthesiology and physiotherapy.- Provides detailed insights into a variety of biomedical instruments for use in different medical areas such as radiology, cardiology and physiotherapy- Considers the advantages, disadvantages and future developments of various biomedical instruments- Equips researchers with an understanding of the working principles of various instruments, thus preparing them for the future development and design of innovative devices in the health domain- Contains various mathematical derivations and numerical data that connect theory with the practical environment- Features a section on patient safety and infection control in relation to the use of biomedical instruments

Artificial Intelligence in e-health Framework, Volume One: AI, Classification, Wearable Devices, and Computer-Aided Diagnosis presents a variety of AI techniques and applications for solving issues in the healthcare industry. As Artificial Intelligence is increasingly incorporated into medical systems and methods, it is critical to understand the formulations and basics of machine and deep learning as well as how to implement these advances into practice. This book specifically explores Artificial Intelligence developments in disease diagnosis, health monitoring, medical image recognition, and diagnostics, as well as e-health records management.This is a valuable resource for health professionals, scientists, researchers, students, and all who wish to broaden their knowledge in this advancing technology.

Provides an in-depth introduction to Artificial Intelligence in e-health frameworkReviews theoretical and application information to develop understanding of AI advances in diagnostics, health monitoring, and records managementDiscusses advanced AI techniques in both machine learning and deep learning for solving healthcare industry issues

Artificial Intelligence in e-health Framework, Volume One: AI, Classification, Wearable Devices, and Computer-Aided Diagnosis presents a variety of AI techniques and applications for solving issues in the healthcare industry. As Artificial Intelligence is increasingly incorporated into medical systems and methods, it is critical to understand the formulations and basics of machine and deep learning as well as how to implement these advances into practice. This book specifically explores Artificial Intelligence developments in disease diagnosis, health monitoring, medical image recognition, and diagnostics, as well as e-health records management.This is a valuable resource for health professionals, scientists, researchers, students, and all who wish to broaden their knowledge in this advancing technology.- Provides an in-depth introduction to Artificial Intelligence in e-health framework- Reviews theoretical and application information to develop understanding of AI advances in diagnostics, health monitoring, and records management- Discusses advanced AI techniques in both machine learning and deep learning for solving healthcare industry issues

Artificial Intelligence in e-health Framework, Volume Two: Telemedicine Applications, Drug Delivery, Augmented Reality, and Robotics offers a comprehensive exploration of AI's role in enhancing medical solutions. Through a focus on machine learning, deep learning, and their practical applications, this book aims to bridge the gap between theoretical advancements and real-world healthcare implementations. In addition to telemedicine advancements, the book explores AI-driven drug discovery, virtual and augmented reality innovations, robotics in medicine, and radiotherapy applications. It serves as an indispensable resource for professionals, researchers, and students eager to deepen their understanding of AI's contributions to modern healthcare. - Provides an in-depth introduction to Artificial Intelligence in an e-health framework- Reviews theoretical and application information to develop our understanding of AI advances in telemedicine, drug development, virtual and augmented reality, robotics, and radiotherapy applications- Discusses advanced AI techniques in both treatment planning and AI-based quality assurance for solving healthcare industry issues

Artificial Intelligence in e-health Framework, Volume Two: Telemedicine Applications, Drug Delivery, Augmented Reality, and Robotics offers a comprehensive exploration of AI’s role in enhancing medical solutions. Through a focus on machine learning, deep learning, and their practical applications, this book aims to bridge the gap between theoretical advancements and real-world healthcare implementations. In addition to telemedicine advancements, the book explores AI-driven drug discovery, virtual and augmented reality innovations, robotics in medicine, and radiotherapy applications. It serves as an indispensable resource for professionals, researchers, and students eager to deepen their understanding of AI's contributions to modern healthcare.

Provides an in-depth introduction to Artificial Intelligence in an e-health frameworkReviews theoretical and application information to develop our understanding of AI advances in telemedicine, drug development, virtual and augmented reality, robotics, and radiotherapy applicationsDiscusses advanced AI techniques in both treatment planning and AI-based quality assurance for solving healthcare industry issues

The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results.

With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques.

Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented.

Key features:

Provides descriptions of different medical imaging modalities and their applications Discusses the basics and advanced aspects of parallel computing with different multicore architectures Expounds on the need for embedding data and task parallelism in different medical image processing techniques Presents helpful examples and case studies of the discussed methods

This book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.

The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results.

With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques.

Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented.

Key features:

Provides descriptions of different medical imaging modalities and their applications Discusses the basics and advanced aspects of parallel computing with different multicore architectures Expounds on the need for embedding data and task parallelism in different medical image processing techniques Presents helpful examples and case studies of the discussed methods

This book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.

Assistive Technology Intervention in Healthcare focuses on various applications of intelligent techniques in biomedical engineering and health informatics. It aims to create awareness about disability reduction and recovery of accidental disability with the help of various rehabilitative systems. Novel technologies in disability treatment, management and assistance, including healthcare devices and their utility from home to hospital, are described. The book deals with simulation, modeling, measurement, control, analysis, information extraction and monitoring of physiological data in clinical medicine and biology.

Features

Covers the latest evolutionary approaches to solve optimization problems in the biomedical engineering field

Explains machine learning–based approaches to improvement in health engineering areas

Reviews the IoT, cloud computing and data analytics in healthcare informatics

Discusses modeling and simulations in the design of biomedical equipment

Explores monitoring of physiological data

This book is aimed at researchers and graduate students in biomedical engineering, clinical engineering and bioinformatics.

Assistive Technology Intervention in Healthcare focuses on various applications of intelligent techniques in biomedical engineering and health informatics. It aims to create awareness about disability reduction and recovery of accidental disability with the help of various rehabilitative systems. Novel technologies in disability treatment, management and assistance, including healthcare devices and their utility from home to hospital, are described. The book deals with simulation, modeling, measurement, control, analysis, information extraction and monitoring of physiological data in clinical medicine and biology.

Features

Covers the latest evolutionary approaches to solve optimization problems in the biomedical engineering field

Explains machine learning–based approaches to improvement in health engineering areas

Reviews the IoT, cloud computing and data analytics in healthcare informatics

Discusses modeling and simulations in the design of biomedical equipment

Explores monitoring of physiological data

This book is aimed at researchers and graduate students in biomedical engineering, clinical engineering and bioinformatics.

Assistive Technology Intervention in Healthcare focuses on various applications of intelligent techniques in biomedical engineering and health informatics. It aims to create awareness about disability reduction and recovery of accidental disability with the help of various rehabilitative systems. Novel technologies in disability treatment, management and assistance, including healthcare devices and their utility from home to hospital, are described. The book deals with simulation, modeling, measurement, control, analysis, information extraction and monitoring of physiological data in clinical medicine and biology. Features Covers the latest evolutionary approaches to solve optimization problems in the biomedical engineering field Explains machine learning–based approaches to improvement in health engineering areas Reviews the IoT, cloud computing and data analytics in healthcare informatics Discusses modeling and simulations in the design of biomedical equipment Explores monitoring of physiological dataThis book is aimed at researchers and graduate students in biomedical engineering, clinical engineering and bioinformatics.

Assistive Technology Intervention in Healthcare focuses on various applications of intelligent techniques in biomedical engineering and health informatics. It aims to create awareness about disability reduction and recovery of accidental disability with the help of various rehabilitative systems. Novel technologies in disability treatment, management and assistance, including healthcare devices and their utility from home to hospital, are described. The book deals with simulation, modeling, measurement, control, analysis, information extraction and monitoring of physiological data in clinical medicine and biology. Features Covers the latest evolutionary approaches to solve optimization problems in the biomedical engineering field Explains machine learning–based approaches to improvement in health engineering areas Reviews the IoT, cloud computing and data analytics in healthcare informatics Discusses modeling and simulations in the design of biomedical equipment Explores monitoring of physiological dataThis book is aimed at researchers and graduate students in biomedical engineering, clinical engineering and bioinformatics.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

This book presents the latest technological advancements in the understanding and diagnosis of neurological disorders. The chapters provide the complexities of diagnosis, understanding pathogenesis, and unveiling the latest therapeutic strategies against neurodegenerative disorders. Innovative Neurodiagnostics and Therapeutics: Understanding and Advancing Treatment for Neurological Disorders also explores the transformative role of optogenetics, nanoarchitectonics, machine learning, and artificial intelligence in neurodiagnosis. Metal organic frameworks are explored for their prospective applications in neuroscience. The book continues to unfold with a focus on robotic advancements in neurotherapeutics, offering comprehensive insights into the cutting edge of neurotherapeutic interventions. The complex interplay between neurodiagnostics and psychodiagnostics is explored in the context of substance use disorders. The book concludes by examining the potential of aquaporin as a therapeutic target in neurological disorders. As such, this book is an invaluable source for neuroscientists, neurologists, and computational biologists.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.

This book presents the latest technological advancements in the understanding and diagnosis of neurological disorders. The chapters provide the complexities of diagnosis, understanding pathogenesis, and unveiling the latest therapeutic strategies against neurodegenerative disorders. The book also explores the transformative role of optogenetics, nanoarchitectonics, machine learning, and artificial intelligence in neurodiagnosis. Metal organic frameworks are explored for their prospective applications in neuroscience. The book continues to unfold with a focus on robotic advancements in neurotherapeutics, offering comprehensive insights into the cutting edge of neurotherapeutic interventions. The complex interplay between neurodiagnostics and psychodiagnostics is explored in the context of substance use disorders. The book concludes by examining the potential of aquaporin as a therapeutic target in neurological disorders. As such, this book is an invaluable source for neuroscientists, neurologists, and computational biologists.

Key Features

Integrates insights from neuroscience, technology, and computational biology to present a holistic understanding of neurological disorders. Explores complexities of diagnosis, pathogenesis, and therapeutic strategies for neurological disorders. Highlights the latest advancements, including optogenetics, nanoarchitectonics, and machine learning for neurodiagnosis. Offers a detailed exploration of robotic advancements in neurotherapeutics. Addresses the complex interplay between neurodiagnostics and psychodiagnostics in substance use disorders.

This book presents the latest technological advancements in the understanding and diagnosis of neurological disorders. The chapters provide the complexities of diagnosis, understanding pathogenesis, and unveiling the latest therapeutic strategies against neurodegenerative disorders. The book also explores the transformative role of optogenetics, nanoarchitectonics, machine learning, and artificial intelligence in neurodiagnosis. Metal organic frameworks are explored for their prospective applications in neuroscience. The book continues to unfold with a focus on robotic advancements in neurotherapeutics, offering comprehensive insights into the cutting edge of neurotherapeutic interventions. The complex interplay between neurodiagnostics and psychodiagnostics is explored in the context of substance use disorders. The book concludes by examining the potential of aquaporin as a therapeutic target in neurological disorders. As such, this book is an invaluable source for neuroscientists, neurologists, and computational biologists.

Key Features

Integrates insights from neuroscience, technology, and computational biology to present a holistic understanding of neurological disorders. Explores complexities of diagnosis, pathogenesis, and therapeutic strategies for neurological disorders. Highlights the latest advancements, including optogenetics, nanoarchitectonics, and machine learning for neurodiagnosis. Offers a detailed exploration of robotic advancements in neurotherapeutics. Addresses the complex interplay between neurodiagnostics and psychodiagnostics in substance use disorders.

The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results.With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques.Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented.Key features:Provides descriptions of different medical imaging modalities and their applicationsDiscusses the basics and advanced aspects of parallel computing with different multicore architecturesExpounds on the need for embedding data and task parallelism in different medical image processing techniquesPresents helpful examples and case studies of the discussed methodsThis book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.

The processing of medical images in a reasonable timeframe and with high definition is very challenging. This volume helps to meet that challenge by presenting a thorough overview of medical imaging modalities, its processing, high-performance computing, and the need to embed parallelism in medical image processing techniques to achieve efficient and fast results.With contributions from researchers from prestigious laboratories and educational institutions, High-Performance Medical Image Processing provides important information on medical image processing techniques, parallel computing techniques, and embedding parallelism in different image processing techniques. A comprehensive review of parallel algorithms in medical image processing problems is a key feature of this book. The volume presents the relevant theoretical frameworks and the latest empirical research findings in the area and provides detailed descriptions about the diverse high-performance techniques.Topics discussed include parallel computing, multicore architectures and their applications in image processing, machine learning applications, conventional and advanced magnetic resonance imaging methods, hyperspectral image processing, algorithms for segmenting 2D slices for 3D viewing, and more. Case studies, such as on the detection of cancer tumors, expound on the information presented.Key features:Provides descriptions of different medical imaging modalities and their applicationsDiscusses the basics and advanced aspects of parallel computing with different multicore architecturesExpounds on the need for embedding data and task parallelism in different medical image processing techniquesPresents helpful examples and case studies of the discussed methodsThis book will be valuable for professionals, researchers, and students working in the field of healthcare engineering, medical imaging technology, applications in machine and deep learning, and more. It is also appropriate for courses in computer engineering, biomedical engineering and electrical engineering based on artificial intelligence, parallel computing, high performance computing, and machine learning and its applications in medical imaging.

Artificial intelligent systems, which offer great improvement in healthcare sector assisted by machine learning, wireless communications, data analytics, cognitive computing, and mobile computing provide more intelligent and convenient solutions and services. With the help of the advanced techniques, now a days it is possible to understand human body and to handle & process the health data anytime and anywhere. It is a smart healthcare system which includes patient, hospital management, doctors, monitoring, diagnosis, decision making modules, disease prevention to meet the challenges and problems arises in healthcare industry. Furthermore, the advanced healthcare systems need to upgrade with new capabilities to provide human with more intelligent and professional healthcare services to further improve the quality of service and user experience. To explore recent advances and disseminate state-of-the-art techniques related to intelligent healthcare services andapplications. This edited book involved in designing systems that will permit the societal acceptance of ambient intelligence including signal processing, imaging, computing, instrumentation, artificial intelligence, internet of health things, data analytics, disease detection, telemedicine, and their applications. As the book includes recent trends in research issues and applications, the contents will be beneficial to Professors, researchers, and engineers. This book will provide support and aid to the researchers involved in designing latest advancements in communication and intelligent systems that will permit the societal acceptance of ambient intelligence. This book presents the latest research being conducted on diverse topics in intelligence technologies with the goal of advancing knowledge and applications healthcare sector and to present the latest snapshot of the ongoing research as well as to shed further light on future directions in this space. The aim of publishing the book is to serve for educators, researchers, and developers working in recent advances and upcoming technologies utilizing computational sciences.

Artificial intelligent systems, which offer great improvement in healthcare sector assisted by machine learning, wireless communications, data analytics, cognitive computing, and mobile computing provide more intelligent and convenient solutions and services. With the help of the advanced techniques, now a days it is possible to understand human body and to handle & process the health data anytime and anywhere. It is a smart healthcare system which includes patient, hospital management, doctors, monitoring, diagnosis, decision making modules, disease prevention to meet the challenges and problems arises in healthcare industry. Furthermore, the advanced healthcare systems need to upgrade with new capabilities to provide human with more intelligent and professional healthcare services to further improve the quality of service and user experience. To explore recent advances and disseminate state-of-the-art techniques related to intelligent healthcare services andapplications. This edited book involved in designing systems that will permit the societal acceptance of ambient intelligence including signal processing, imaging, computing, instrumentation, artificial intelligence, internet of health things, data analytics, disease detection, telemedicine, and their applications. As the book includes recent trends in research issues and applications, the contents will be beneficial to Professors, researchers, and engineers. This book will provide support and aid to the researchers involved in designing latest advancements in communication and intelligent systems that will permit the societal acceptance of ambient intelligence. This book presents the latest research being conducted on diverse topics in intelligence technologies with the goal of advancing knowledge and applications healthcare sector and to present the latest snapshot of the ongoing research as well as to shed further light on future directions in this space. The aim of publishing the book is to serve for educators, researchers, and developers working in recent advances and upcoming technologies utilizing computational sciences.