Machine Learning For Biomedical Applications
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Machine Learning For Biomedical Applications
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Author : Maria Deprez
language : en
Publisher: Academic Press
Release Date : 2023-09-12
Machine Learning For Biomedical Applications written by Maria Deprez and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-12 with Computers categories.
Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians. Gives a basic understanding of the most fundamental concepts within machine learning and their role in biomedical data analysis Shows how to apply a range of commonly used machine learning and deep learning techniques in biomedical problems Develops practical computational skills that are needed to manipulate complex biomedical data sets Shows how to design machine learning experiments that address specific problems related to biomedical data
Deep Learning For Biomedical Applications
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Author : Utku Kose
language : en
Publisher: CRC Press
Release Date : 2021-07-20
Deep Learning For Biomedical Applications written by Utku Kose and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-20 with Technology & Engineering categories.
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.
Machine Learning For Biomedical Applications From Crowdsourcing To Deep Learning
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Author : Shadi N. M. Albarqouni
language : en
Publisher:
Release Date : 2017
Machine Learning For Biomedical Applications From Crowdsourcing To Deep Learning written by Shadi N. M. Albarqouni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Explainable Artificial Intelligence For Biomedical Applications
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Author : Utku Kose
language : en
Publisher: CRC Press
Release Date : 2023-12-14
Explainable Artificial Intelligence For Biomedical Applications written by Utku Kose and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-14 with Technology & Engineering categories.
Since its first appearance, artificial intelligence has been ensuring revolutionary outcomes in the context of real-world problems. At this point, it has strong relations with biomedical and today’s intelligent systems compete with human capabilities in medical tasks. However, advanced use of artificial intelligence causes intelligent systems to be black-box. That situation is not good for building trustworthy intelligent systems in medical applications. For a remarkable amount of time, researchers have tried to solve the black-box issue by using modular additions, which have led to the rise of the term: interpretable artificial intelligence. As the literature matured (as a result of, in particular, deep learning), that term transformed into explainable artificial intelligence (XAI). This book provides an essential edited work regarding the latest advancements in explainable artificial intelligence (XAI) for biomedical applications. It includes not only introductive perspectives but also applied touches and discussions regarding critical problems as well as future insights. Topics discussed in the book include: XAI for the applications with medical images XAI use cases for alternative medical data/task Different XAI methods for biomedical applications Reviews for the XAI research for critical biomedical problems. Explainable Artificial Intelligence for Biomedical Applications is ideal for academicians, researchers, students, engineers, and experts from the fields of computer science, biomedical, medical, and health sciences. It also welcomes all readers of different fields to be informed about use cases of XAI in black-box artificial intelligence. In this sense, the book can be used for both teaching and reference source purposes.
Handbook Of Deep Learning In Biomedical Engineering
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Author : Valentina Emilia Balas
language : en
Publisher: Academic Press
Release Date : 2020-11-12
Handbook Of Deep Learning In Biomedical Engineering written by Valentina Emilia Balas and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-12 with Science categories.
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 For Data Analytics
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Author : Himansu Das
language : en
Publisher: Academic Press
Release Date : 2020-05-29
Deep Learning For Data Analytics written by Himansu Das and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-29 with Science categories.
Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning
Internet Of Things Enabled Machine Learning For Biomedical Application
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Author : Neha Goel
language : en
Publisher:
Release Date : 2024-08-14
Internet Of Things Enabled Machine Learning For Biomedical Application written by Neha Goel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-14 with Computers categories.
The text begins by highlighting the benefits of the internet of things enabled machine learning in the healthcare sector, examines the diagnosis of diseases using machine learning algorithms, and examines security and privacy issues in the healthcare systems using the internet of things. The text elaborates on image processing implementation for medical images to detect and classify diseases based on magnetic resonance imaging and ultrasound images. This book: - Covers the procedure to recognize emotions using image processing and the internet of things-enabled machine learning. - Highlights security and privacy issues in the healthcare system using the internet of things. - Discusses classification and implementation techniques of image segmentation. - Explains different algorithms of machine learning for image processing in a comprehensive manner. - Provides computational intelligence on the internet of things for future biomedical applications including lung cancer. It is primarily written for graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.
Computational Learning Approaches To Data Analytics In Biomedical Applications
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Author : Khalid Al-Jabery
language : en
Publisher: Academic Press
Release Date : 2019-11-20
Computational Learning Approaches To Data Analytics In Biomedical Applications written by Khalid Al-Jabery and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-20 with Technology & Engineering categories.
Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. Includes an overview of data analytics in biomedical applications and current challenges Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices Provides complete coverage of computational and statistical analysis tools for biomedical data analysis Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor
Deep Learning For Biomedical Data Analysis
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Author : Mourad Elloumi
language : en
Publisher: Springer Nature
Release Date : 2021-07-13
Deep Learning For Biomedical Data Analysis written by Mourad Elloumi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-13 with Medical categories.
This book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of DL for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine fundamental theory of Artificial Intelligence (AI), Machine Learning (ML) and DL with practical applications in Biology and Medicine. Certainly, the list of topics covered in this book is not exhaustive but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data analysis either focus on specific topics or lack technical depth. The chapters presented in this book were selected for quality and relevance. The book also presents experiments that provide qualitative and quantitative overviews in the field of biomedical data analysis. The reader will require some familiarity with AI, ML and DL and will learn about techniques and approaches that deal with the most important and/or the newest topics encountered in the field of DL for biomedical data analysis. He/she will discover both the fundamentals behind DL techniques and approaches, and their applications on biomedical data. This book can also serve as a reference book for graduate courses in Bioinformatics, AI, ML and DL. The book aims not only at professional researchers and practitioners but also graduate students, senior undergraduate students and young researchers. This book will certainly show the way to new techniques and approaches to make new discoveries.
Machine Learning For Healthcare Applications
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Author : Sachi Nandan Mohanty
language : en
Publisher: John Wiley & Sons
Release Date : 2021-04-13
Machine Learning For Healthcare Applications written by Sachi Nandan Mohanty and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-13 with Computers categories.
When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.