Handbook of Deep Learning in Biomedical Engineering

Book Handbook of Deep Learning in Biomedical Engineering Cover

Read or download book entitled Handbook of Deep Learning in Biomedical Engineering written by Valentina Emilia Balas and published by Academic Press in PDF, EPUB and Kindle Format. Click Get This Book button to download or read online books. Join over 650.000 happy Readers and READ as many books as you like. We cannot guarantee that Handbook of Deep Learning in Biomedical Engineering book is available in the library.

  • Publisher : Academic Press
  • Release : 12 November 2020
  • ISBN : 9780128230473
  • Page : 320 pages
  • Rating : 4.5/5 from 103 voters

Download Handbook of Deep Learning in Biomedical Engineering in PDF, Epub and Kindle

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

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Handbook of Deep Learning in Biomedical Engineering

Handbook of Deep Learning in Biomedical Engineering
  • Author : Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
  • Publisher : Academic Press
  • Release Date : 2020-11-12
  • ISBN : 9780128230473
GET THIS BOOKHandbook of Deep Learning in Biomedical Engineering

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

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
  • Publisher : CRC Press
  • Release Date : 2021-09-22
  • ISBN : 9781000370492
GET THIS BOOKHandbook of Deep Learning in Biomedical Engineering and Health Informatics

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

Handbook of Artificial Intelligence in Biomedical Engineering

Handbook of Artificial Intelligence in Biomedical Engineering
  • Author : Krishnan Saravanan,Ramesh Kesavan,B. Surendiran,G. S. Mahalakshmi
  • Publisher : Unknown
  • Release Date : 2021
  • ISBN : 1003045561
GET THIS BOOKHandbook of Artificial Intelligence in Biomedical Engineering

"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

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

Handbook of Deep Learning in Biomedical Engineering and Health Informatics
  • Author : Golden Julie,S. M. Jaisakthi,Y. Harold Robinson
  • Publisher : Unknown
  • Release Date : 2021
  • ISBN : 1774638177
GET THIS BOOKHandbook of Deep Learning in Biomedical Engineering and Health Informatics

"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

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
  • Publisher : Academic Press
  • Release Date : 2021-04-08
  • ISBN : 9780128222614
GET THIS BOOKHandbook of Computational Intelligence in Biomedical Engineering and Healthcare

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

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
  • Publisher : Academic Press
  • Release Date : 2019-11-13
  • ISBN : 9780128183199
GET THIS BOOKHandbook of Data Science Approaches for Biomedical Engineering

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

Handbook of Deep Learning Applications

Handbook of Deep Learning Applications
  • Author : Valentina Emilia Balas,Sanjiban Sekhar Roy,Dharmendra Sharma,Pijush Samui
  • Publisher : Springer
  • Release Date : 2019-02-25
  • ISBN : 9783030114794
GET THIS BOOKHandbook of Deep Learning Applications

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in

Handbook of Machine Learning for Computational Optimization

Handbook of Machine Learning for Computational Optimization
  • Author : Vishal Jain,Sapna Juneja,Abhinav Juneja,Ramani Kannan
  • Publisher : CRC Press
  • Release Date : 2021-11-02
  • ISBN : 9781000455687
GET THIS BOOKHandbook of Machine Learning for Computational Optimization

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

Handbook of Neural Computation

Handbook of Neural Computation
  • Author : Pijush Samui,Sanjiban Sekhar Roy,Valentina E. Balas
  • Publisher : Academic Press
  • Release Date : 2017-07-18
  • ISBN : 9780128113196
GET THIS BOOKHandbook of Neural Computation

Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor

Deep Learning for Biomedical Applications

Deep Learning for Biomedical Applications
  • Author : Utku Kose,Omer Deperlioglu,D. Jude Hemanth
  • Publisher : CRC Press
  • Release Date : 2021-07-20
  • ISBN : 9781000406429
GET THIS BOOKDeep Learning for Biomedical Applications

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,

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
  • Publisher : IGI Global
  • Release Date : 2020-02-28
  • ISBN : 9781799821229
GET THIS BOOKHandbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering

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

Advances in Deep Learning for Medical Image Analysis

Advances in Deep Learning for Medical Image Analysis
  • Author : Archana Mire,Vinayak Elangovan,Shailaja Patil
  • Publisher : CRC Press
  • Release Date : 2022-04-28
  • ISBN : 9781000575958
GET THIS BOOKAdvances in Deep Learning for Medical Image Analysis

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,

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
  • Publisher : IGI Global, Engineering Science Reference
  • Release Date : 2019-07
  • ISBN : 1522599029
GET THIS BOOKHandbook of Research on Applications and Implementations of Machine Learning Techniques

"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 Research on Emerging Trends and Applications of Machine Learning

Handbook of Research on Emerging Trends and Applications of Machine Learning
  • Author : Solanki, Arun,Kumar, Sandeep,Nayyar, Anand
  • Publisher : IGI Global
  • Release Date : 2019-12-13
  • ISBN : 9781522596455
GET THIS BOOKHandbook of Research on Emerging Trends and Applications of Machine Learning

As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world.

Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics

Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics
  • Author : Sujata Dash,Subhendu Kumar Pani,Joel J. P. C. Rodrigues,Babita Majhi
  • Publisher : CRC Press
  • Release Date : 2022-02-11
  • ISBN : 9781000534054
GET THIS BOOKDeep Learning, Machine Learning and IoT in Biomedical and Health Informatics

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions