Handbook of Deep Learning in Biomedical Engineering

Handbook of Deep Learning in Biomedical Engineering
Author: Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
Publsiher: Academic Press
Total Pages: 320
Release: 2020-11-12
ISBN 10: 0128230479
ISBN 13: 9780128230473
Language: EN, FR, DE, ES & NL

Handbook of Deep Learning in Biomedical Engineering Book Review:

Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer’s, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis. Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

Handbook of Deep Learning in Biomedical Engineering and Health Informatics
Author: E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Publsiher: CRC Press
Total Pages: 344
Release: 2021-09-22
ISBN 10: 1000370496
ISBN 13: 9781000370492
Language: EN, FR, DE, ES & NL

Handbook of Deep Learning in Biomedical Engineering and Health Informatics Book Review:

This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease. This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat patients more effectively. Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. This volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc. Key features: Introduces important recent technological advancements in the field Describes the various techniques, platforms, and tools used in biomedical deep learning systems Includes informative case studies that help to explain the new technologies Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students.

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

Handbook of Deep Learning in Biomedical Engineering and Health Informatics
Author: E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Publsiher: CRC Press
Total Pages: 344
Release: 2021-09-22
ISBN 10: 1000370453
ISBN 13: 9781000370454
Language: EN, FR, DE, ES & NL

Handbook of Deep Learning in Biomedical Engineering and Health Informatics Book Review:

This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease. This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat patients more effectively. Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. This volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc. Key features: Introduces important recent technological advancements in the field Describes the various techniques, platforms, and tools used in biomedical deep learning systems Includes informative case studies that help to explain the new technologies Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students.

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare
Author: Janmenjoy Nayak,Bighnaraj Naik,Danilo Pelusi,Asit Kumar Das
Publsiher: Academic Press
Total Pages: 396
Release: 2021-04-08
ISBN 10: 0128222611
ISBN 13: 9780128222614
Language: EN, FR, DE, ES & NL

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare Book Review:

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques. Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/unstructured data on experimental analysis. Presents a comprehensive handbook that covers an Introduction to Computational Intelligence in Biomedical Engineering and Healthcare, Computational Intelligence Techniques, and Advanced and Emerging Techniques in Computational Intelligence Helps readers analyze and do advanced research in specialty healthcare applications Includes links to websites, videos, articles and other online content to expand and support primary learning objectives

Handbook of Artificial Intelligence in Biomedical Engineering

Handbook of Artificial Intelligence in Biomedical Engineering
Author: Krishnan Saravanan,Ramesh Kesavan,B. Surendiran,G. S. Mahalakshmi
Publsiher: Unknown
Total Pages: 538
Release: 2021
ISBN 10: 9781003045564
ISBN 13: 1003045561
Language: EN, FR, DE, ES & NL

Handbook of Artificial Intelligence in Biomedical Engineering Book Review:

"Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert's knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications. The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts. Topics include: Security and privacy issues in biomedical AI systems and potential solutions Healthcare applications using biomedical AI systems Machine learning in biomedical engineering Live patient monitoring systems Semantic annotation of healthcare data This book presents a broad exploration of biomedical systems using artificial intelligence techniques with detailed coverage of the applications, techniques, algorithms, platforms, and tools in biomedical AI systems. This book will benefit researchers, medical and industry practitioners, academicians, and students"--

Handbook of Data Science Approaches for Biomedical Engineering

Handbook of Data Science Approaches for Biomedical Engineering
Author: Valentina Emilia Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari
Publsiher: Academic Press
Total Pages: 318
Release: 2019-11-13
ISBN 10: 0128183195
ISBN 13: 9780128183199
Language: EN, FR, DE, ES & NL

Handbook of Data Science Approaches for Biomedical Engineering Book Review:

Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more

Handbook of Artificial Intelligence in Biomedical Engineering

Handbook of Artificial Intelligence in Biomedical Engineering
Author: Saravanan Krishnan,Ramesh Kesavan,B. Surendiran,G. S. Mahalakshmi
Publsiher: Apple Academic Press
Total Pages: 622
Release: 2020-12-15
ISBN 10: 9781771889209
ISBN 13: 1771889209
Language: EN, FR, DE, ES & NL

Handbook of Artificial Intelligence in Biomedical Engineering Book Review:

"Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert's knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications. The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts. Topics include: Security and privacy issues in biomedical AI systems and potential solutions Healthcare applications using biomedical AI systems Machine learning in biomedical engineering Live patient monitoring systems Semantic annotation of healthcare data This book presents a broad exploration of biomedical systems using artificial intelligence techniques with detailed coverage of the applications, techniques, algorithms, platforms, and tools in biomedical AI systems. This book will benefit researchers, medical and industry practitioners, academicians, and students"--

Advances in Deep Learning for Medical Image Analysis

Advances in Deep Learning for Medical Image Analysis
Author: Archana Mire,Vinayak Elangovan,Shailaja Patil
Publsiher: CRC Press
Total Pages: 168
Release: 2022-04-28
ISBN 10: 1000575950
ISBN 13: 9781000575958
Language: EN, FR, DE, ES & NL

Advances in Deep Learning for Medical Image Analysis Book Review:

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

Handbook of Machine Learning for Computational Optimization

Handbook of Machine Learning for Computational Optimization
Author: Vishal Jain,Sapna Juneja,Abhinav Juneja,Ramani Kannan
Publsiher: CRC Press
Total Pages: 294
Release: 2021-11-02
ISBN 10: 1000455688
ISBN 13: 9781000455687
Language: EN, FR, DE, ES & NL

Handbook of Machine Learning for Computational Optimization Book Review:

Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers.

Machine Learning and Deep Learning Techniques for Medical Science

Machine Learning and Deep Learning Techniques for Medical Science
Author: K. Gayathri Devi
Publsiher: CRC Press
Total Pages: 410
Release: 2022
ISBN 10: 9781003217497
ISBN 13: 1003217494
Language: EN, FR, DE, ES & NL

Machine Learning and Deep Learning Techniques for Medical Science Book Review:

The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector. Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).

Deep Learning for Biomedical Applications

Deep Learning for Biomedical Applications
Author: Utku Kose,Omer Deperlioglu,D. Jude Hemanth
Publsiher: CRC Press
Total Pages: 364
Release: 2021-07-20
ISBN 10: 1000406423
ISBN 13: 9781000406429
Language: EN, FR, DE, ES & NL

Deep Learning for Biomedical Applications Book Review:

This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.

The Role of the Internet of Things IoT in Biomedical Engineering

The Role of the Internet of Things  IoT  in Biomedical Engineering
Author: Sushree Bibhuprada B. Priyadarshini,Devendra Kumar Sharma,Rohit Sharma,Korhan Cengiz
Publsiher: CRC Press
Total Pages: 274
Release: 2022-02-17
ISBN 10: 1000400646
ISBN 13: 9781000400649
Language: EN, FR, DE, ES & NL

The Role of the Internet of Things IoT in Biomedical Engineering Book Review:

This volume introduces the key evolving applications of IoT in the medical field for patient care delivery through the usage of smart devices. It shows how IoT opens the door to a wealth of relevant healthcare information through real-time data analysis as well as testing, providing reliable and pragmatic data that yields enhanced solutions and discovery of previously undiscovered issues. This new volume discusses IoT devices that are deployed for enabling patient health tracking, various emergency issues, smart administration of patients, etc. It looks at the problems of cardiac analysis in e-healthcare, explores the employment of smart devices aimed for different patient issues, and examines the usage of Arduino kits where the data can be transferred to the cloud for internet-based uses. The volume also considers the roles of IoT in electroencephalography (EEG) and magnetic resonance imaging (MRI), which play significant roles in biomedical applications. This book also incorporates the use of IoT applications for smart wheelchairs, telemedicine, GPS positioning of heart patients, smart administration with drug tracking, and more.

Deep Learning

Deep Learning
Author: Shriram K. Vasudevan,Sini Raj Pulari,Subashri Vasudevan
Publsiher: Chapman & Hall/CRC
Total Pages: 290
Release: 2021-12
ISBN 10: 9781003185635
ISBN 13: 1003185630
Language: EN, FR, DE, ES & NL

Deep Learning Book Review:

Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding - and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively. Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader's understanding Includes references to the related YouTube videos that provide additional guidance AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.

Data Analytics in Biomedical Engineering and Healthcare

Data Analytics in Biomedical Engineering and Healthcare
Author: Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar
Publsiher: Academic Press
Total Pages: 292
Release: 2020-10-18
ISBN 10: 0128193158
ISBN 13: 9780128193150
Language: EN, FR, DE, ES & NL

Data Analytics in Biomedical Engineering and Healthcare Book Review:

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
Author: Sisodia, Dilip Singh,Pachori, Ram Bilas,Garg, Lalit
Publsiher: IGI Global
Total Pages: 420
Release: 2020-02-28
ISBN 10: 1799821226
ISBN 13: 9781799821229
Language: EN, FR, DE, ES & NL

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering Book Review:

Artificial intelligence (AI) is revolutionizing every aspect of human life including human healthcare and wellbeing management. Various types of intelligent healthcare engineering applications have been created that help to address patient healthcare and outcomes such as identifying diseases and gathering patient information. Advancements in AI applications in healthcare continue to be sought to aid rapid disease detection, health monitoring, and prescription drug tracking. TheHandbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering is an essential scholarly publication that provides comprehensive research on the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of healthcare engineering solutions. Featuring a wide range of topics such as genetic algorithms, mobile robotics, and neuroinformatics, this book is ideal for engineers, technology developers, IT consultants, hospital administrators, academicians, healthcare professionals, practitioners, researchers, and students.

High Performance Medical Image Processing

High Performance Medical Image Processing
Author: Sanjay Saxena,Sudip Paul
Publsiher: CRC Press
Total Pages: 328
Release: 2022-07-07
ISBN 10: 1000410358
ISBN 13: 9781000410358
Language: EN, FR, DE, ES & NL

High Performance Medical Image Processing Book Review:

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.

Handbook of Research on Applications and Implementations of Machine Learning Techniques

Handbook of Research on Applications and Implementations of Machine Learning Techniques
Author: Sathiyamoorthi Velayutham
Publsiher: IGI Gloval, Engineering Science Reference
Total Pages: 461
Release: 2019-07
ISBN 10: 9781522599029
ISBN 13: 1522599029
Language: EN, FR, DE, ES & NL

Handbook of Research on Applications and Implementations of Machine Learning Techniques Book Review:

"This book examines the practical applications and implementation of various machine learning techniques in various fields such as agriculture, medical, image processing, and networking"--

Handbook of Neural Engineering

Handbook of Neural Engineering
Author: Metin Akay
Publsiher: John Wiley & Sons
Total Pages: 800
Release: 2007-01-09
ISBN 10: 0470068280
ISBN 13: 9780470068281
Language: EN, FR, DE, ES & NL

Handbook of Neural Engineering Book Review:

An important new work establishing a foundation for future developments in neural engineering The Handbook of Neural Engineering provides theoretical foundations in computational neural science and engineering and current applications in wearable and implantable neural sensors/probes. Inside, leading experts from diverse disciplinary groups representing academia, industry, and private and government organizations present peer-reviewed contributions on the brain-computer interface, nano-neural engineering, neural prostheses, imaging the brain, neural signal processing, the brain, and neurons. The Handbook of Neural Engineering covers: Neural signal and image processing--the analysis and modeling of neural activity and EEG-related activities using the nonlinear and nonstationary analysis methods, including the chaos, fractal, and time-frequency and time-scale analysis methods--and how to measure functional, physiological, and metabolic activities in the human brain using current and emerging medical imaging technologies Neuro-nanotechnology, artificial implants, and neural prosthesis--the design of multi-electrode arrays to study how the neurons of human and animals encode stimuli, the evaluation of functional changes in neural networks after stroke and spinal cord injuries, and improvements in therapeutic applications using neural prostheses Neurorobotics and neural rehabilitation engineering--the recent developments in the areas of biorobotic system, biosonar head, limb kinematics, and robot-assisted activity to improve the treatment of elderly subjects at the hospital and home, as well as the interactions of the neuron chip, neural information processing, perception and neural dynamics, learning memory and behavior, biological neural networks, and neural control

Artificial Intelligence in Medical Imaging

Artificial Intelligence in Medical Imaging
Author: Lia Morra,Silvia Delsanto,Loredana Correale
Publsiher: CRC Press
Total Pages: 152
Release: 2019-11-25
ISBN 10: 1000753085
ISBN 13: 9781000753080
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Medical Imaging Book Review:

This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective

Deep Learning in Biomedical and Health Informatics

Deep Learning in Biomedical and Health Informatics
Author: M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari
Publsiher: CRC Press
Total Pages: 224
Release: 2021-09-26
ISBN 10: 1000429083
ISBN 13: 9781000429084
Language: EN, FR, DE, ES & NL

Deep Learning in Biomedical and Health Informatics Book Review:

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques. In short, the volume : Discusses the relationship between AI and healthcare, and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA. Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal. Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.