Handbook Of Deep Learning In Biomedical Engineering And Health Informatics

Handbook Of Deep Learning In Biomedical Engineering And Health Informatics Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Handbook Of Deep Learning In Biomedical Engineering And Health Informatics book. This book definitely worth reading, it is an incredibly well-written.

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Handbook of Deep Learning in Biomedical Engineering and Health Informatics Book PDF

Author : E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Publisher : CRC Press
Page : 344 pages
File Size : 55,5 Mb
Release : 2021-09-22
Category : Medical
ISBN : 9781000370454

Get Book

Handbook of Deep Learning in Biomedical Engineering and Health Informatics by E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi Pdf

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.

Deep Learning Techniques for Biomedical and Health Informatics

Basant Agarwal,Valentina Emilia Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma
Deep Learning Techniques for Biomedical and Health Informatics Book PDF

Author : Basant Agarwal,Valentina Emilia Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma
Publisher : Academic Press
Page : 367 pages
File Size : 54,9 Mb
Release : 2020-01-14
Category : Science
ISBN : 9780128190623

Get Book

Deep Learning Techniques for Biomedical and Health Informatics by Basant Agarwal,Valentina Emilia Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma Pdf

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis

Handbook of Deep Learning in Biomedical Engineering

Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
Handbook of Deep Learning in Biomedical Engineering Book PDF

Author : Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
Publisher : Academic Press
Page : 320 pages
File Size : 42,6 Mb
Release : 2020-11-12
Category : Science
ISBN : 9780128230473

Get Book

Handbook of Deep Learning in Biomedical Engineering by Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar Pdf

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

Deep Learning in Biomedical and Health Informatics

M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari
Deep Learning in Biomedical and Health Informatics Book PDF

Author : M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari
Publisher : CRC Press
Page : 224 pages
File Size : 48,7 Mb
Release : 2021-09-26
Category : Computers
ISBN : 9781000429084

Get Book

Deep Learning in Biomedical and Health Informatics by M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari Pdf

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.

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

Sujata Dash,Subhendu Kumar Pani,Joel J. P. C. Rodrigues,Babita Majhi
Deep Learning Machine Learning and IoT in Biomedical and Health Informatics Book PDF

Author : Sujata Dash,Subhendu Kumar Pani,Joel J. P. C. Rodrigues,Babita Majhi
Publisher : CRC Press
Page : 407 pages
File Size : 46,9 Mb
Release : 2022-02-10
Category : Computers
ISBN : 9781000534054

Get Book

Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics by Sujata Dash,Subhendu Kumar Pani,Joel J. P. C. Rodrigues,Babita Majhi Pdf

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 are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others. • Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Handbook of Deep Learning in Biomedical Engineering and Health Informatics Book PDF

Author : E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Publisher : CRC Press
Page : 366 pages
File Size : 49,8 Mb
Release : 2021-09-22
Category : Medical
ISBN : 9781000370492

Get Book

Handbook of Deep Learning in Biomedical Engineering and Health Informatics by E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi Pdf

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 on Intelligent Healthcare Analytics

A. Jaya,K. Kalaiselvi,Dinesh Goyal,Dhiya Al-Jumeily
Handbook on Intelligent Healthcare Analytics Book PDF

Author : A. Jaya,K. Kalaiselvi,Dinesh Goyal,Dhiya Al-Jumeily
Publisher : John Wiley & Sons
Page : 452 pages
File Size : 51,6 Mb
Release : 2022-06-01
Category : Technology & Engineering
ISBN : 9781119791799

Get Book

Handbook on Intelligent Healthcare Analytics by A. Jaya,K. Kalaiselvi,Dinesh Goyal,Dhiya Al-Jumeily Pdf

HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, the reader will find in this Handbook: Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning; An exploration of predictive analytics in healthcare; The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics. Audience Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

Janmenjoy Nayak,Bighnaraj Naik,Danilo Pelusi,Asit Kumar Das
Handbook of Computational Intelligence in Biomedical Engineering and Healthcare Book PDF

Author : Janmenjoy Nayak,Bighnaraj Naik,Danilo Pelusi,Asit Kumar Das
Publisher : Academic Press
Page : 398 pages
File Size : 47,7 Mb
Release : 2021-04-08
Category : Science
ISBN : 9780128222614

Get Book

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare by Janmenjoy Nayak,Bighnaraj Naik,Danilo Pelusi,Asit Kumar Das Pdf

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

Explainable AI in Healthcare

Mehul S Raval,Mohendra Roy,Tolga Kaya,Rupal Kapdi
Explainable AI in Healthcare Book PDF

Author : Mehul S Raval,Mohendra Roy,Tolga Kaya,Rupal Kapdi
Publisher : CRC Press
Page : 346 pages
File Size : 47,7 Mb
Release : 2023-07-17
Category : Medical
ISBN : 9781000906400

Get Book

Explainable AI in Healthcare by Mehul S Raval,Mohendra Roy,Tolga Kaya,Rupal Kapdi Pdf

This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering. This book will benefit readers in the following ways: Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care Investigates bridges between computer scientists and physicians being built with XAI Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent Initiates discussions on human-AI relationships in health care Unites learning for privacy preservation in health care

Machine Learning in Healthcare Informatics

Sumeet Dua,U. Rajendra Acharya,Prerna Dua
Machine Learning in Healthcare Informatics Book PDF

Author : Sumeet Dua,U. Rajendra Acharya,Prerna Dua
Publisher : Springer Science & Business Media
Page : 332 pages
File Size : 47,7 Mb
Release : 2013-12-09
Category : Technology & Engineering
ISBN : 9783642400179

Get Book

Machine Learning in Healthcare Informatics by Sumeet Dua,U. Rajendra Acharya,Prerna Dua Pdf

The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.

Medical Data Analysis and Processing using Explainable Artificial Intelligence

Om Prakash Jena,Mrutyunjaya Panda,Utku Kose
Medical Data Analysis and Processing using Explainable Artificial Intelligence Book PDF

Author : Om Prakash Jena,Mrutyunjaya Panda,Utku Kose
Publisher : CRC Press
Page : 287 pages
File Size : 44,9 Mb
Release : 2023-11-02
Category : Technology & Engineering
ISBN : 9781000983654

Get Book

Medical Data Analysis and Processing using Explainable Artificial Intelligence by Om Prakash Jena,Mrutyunjaya Panda,Utku Kose Pdf

The text presents concepts of explainable artificial intelligence (XAI) in solving real world biomedical and healthcare problems. It will serve as an ideal reference text for graduate students and academic researchers in diverse fields of engineering including electrical, electronics and communication, computer, and biomedical. Presents explainable artificial intelligence (XAI) based machine analytics and deep learning in medical science. Discusses explainable artificial intelligence (XA)I with the Internet of Medical Things (IoMT) for healthcare applications. Covers algorithms, tools, and frameworks for explainable artificial intelligence on medical data. Explores the concepts of natural language processing and explainable artificial intelligence (XAI) on medical data processing. Discusses machine learning and deep learning scalability models in healthcare systems. This text focuses on data driven analysis and processing of advanced methods and techniques with the help of explainable artificial intelligence (XAI) algorithms. It covers machine learning, Internet of Things (IoT), and deep learning algorithms based on XAI techniques for medical data analysis and processing. The text will present different dimensions of XAI based computational intelligence applications. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and biomedical engineering.

Machine Learning for Health Informatics

Andreas Holzinger
Machine Learning for Health Informatics Book PDF

Author : Andreas Holzinger
Publisher : Springer
Page : 481 pages
File Size : 50,6 Mb
Release : 2016-12-09
Category : Computers
ISBN : 9783319504780

Get Book

Machine Learning for Health Informatics by Andreas Holzinger Pdf

Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.

Machine Learning and Deep Learning Techniques for Medical Science

K. Gayathri Devi,Kishore Balasubramanian,Le Anh Ngoc
Machine Learning and Deep Learning Techniques for Medical Science Book PDF

Author : K. Gayathri Devi,Kishore Balasubramanian,Le Anh Ngoc
Publisher : CRC Press
Page : 351 pages
File Size : 40,6 Mb
Release : 2022-05-11
Category : Technology & Engineering
ISBN : 9781000583366

Get Book

Machine Learning and Deep Learning Techniques for Medical Science by K. Gayathri Devi,Kishore Balasubramanian,Le Anh Ngoc Pdf

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 Medical Image Analysis

S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Deep Learning for Medical Image Analysis Book PDF

Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publisher : Academic Press
Page : 458 pages
File Size : 46,5 Mb
Release : 2017-01-18
Category : Computers
ISBN : 9780128104095

Get Book

Deep Learning for Medical Image Analysis by S. Kevin Zhou,Hayit Greenspan,Dinggang Shen Pdf

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

Artificial Intelligence for Innovative Healthcare Informatics

Shabir Ahmad Parah,Mamoon Rashid,Vijayakumar Varadarajan
Artificial Intelligence for Innovative Healthcare Informatics Book PDF

Author : Shabir Ahmad Parah,Mamoon Rashid,Vijayakumar Varadarajan
Publisher : Springer Nature
Page : 320 pages
File Size : 42,6 Mb
Release : 2022-05-23
Category : Medical
ISBN : 9783030965693

Get Book

Artificial Intelligence for Innovative Healthcare Informatics by Shabir Ahmad Parah,Mamoon Rashid,Vijayakumar Varadarajan Pdf

There are several popular books published in Healthcare Computational Informatics like Computational Bioengineering and Bioinformatics (2020), Springer; Health Informatics (2017), Springer; Health Informatics Vision: From Data via Information to Knowledge (2019), IOS Press; Data Analytics in Biomedical Engineering and Healthcare (2020), Elsevier. However, in all these mentioned books, the challenges in Biomedical Imaging are solved in one dimension by use of any specific technology like Image Processing, Machine Learning or Computer Aided Systems. In this book, the book it has been attempted to bring all technologies related to computational analytics together and apply them on Biomedical Imaging.