Machine Learning For Brain Disorders

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Machine Learning

Author : Andrea Mechelli
Publisher : Academic Press
ISBN 13 : 0128157402
Total Pages : 412 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Machine Learning by : Andrea Mechelli

Download or read book Machine Learning written by Andrea Mechelli and published by Academic Press. This book was released on 2019-11-14 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. - Provides a non-technical introduction to machine learning and applications to brain disorders - Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches - Covers the main methodological challenges in the application of machine learning to brain disorders - Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python

Machine Learning for Brain Disorders

Author : Olivier Colliot
Publisher : Springer Nature
ISBN 13 : 1071631950
Total Pages : 1058 pages
Book Rating : 4.0/5 (716 download)

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Book Synopsis Machine Learning for Brain Disorders by : Olivier Colliot

Download or read book Machine Learning for Brain Disorders written by Olivier Colliot and published by Springer Nature. This book was released on 2023-07-24 with total page 1058 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Open Access volume provides readers with an up-to-date and comprehensive guide to both methodological and applicative aspects of machine learning (ML) for brain disorders. The chapters in this book are organized into five parts. Part One presents the fundamentals of ML. Part Two looks at the main types of data used to characterize brain disorders, including clinical assessments, neuroimaging, electro- and magnetoencephalography, genetics and omics data, electronic health records, mobile devices, connected objects and sensors. Part Three covers the core methodologies of ML in brain disorders and the latest techniques used to study them. Part Four is dedicated to validation and datasets, and Part Five discusses applications of ML to various neurological and psychiatric disorders. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Comprehensive and cutting, Machine Learning for Brain Disorders is a valuable resource for researchers and graduate students who are new to this field, as well as experienced researchers who would like to further expand their knowledge in this area. This book will be useful to students and researchers from various backgrounds such as engineers, computer scientists, neurologists, psychiatrists, radiologists, and neuroscientists.

Early Detection of Neurological Disorders Using Machine Learning Systems

Author : Paul, Sudip
Publisher : IGI Global
ISBN 13 : 1522585680
Total Pages : 376 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Early Detection of Neurological Disorders Using Machine Learning Systems by : Paul, Sudip

Download or read book Early Detection of Neurological Disorders Using Machine Learning Systems written by Paul, Sudip and published by IGI Global. This book was released on 2019-06-28 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: While doctors and physicians are more than capable of detecting diseases of the brain, the most agile human mind cannot compete with the processing power of modern technology. Utilizing algorithmic systems in healthcare in this way may provide a way to treat neurological diseases before they happen. Early Detection of Neurological Disorders Using Machine Learning Systems provides innovative insights into implementing smart systems to detect neurological diseases at a faster rate than by normal means. The topics included in this book are artificial intelligence, data analysis, and biomedical informatics. It is designed for clinicians, doctors, neurologists, physiotherapists, neurorehabilitation specialists, scholars, academics, and students interested in topics centered on biomedical engineering, bio-electronics, medical electronics, physiology, neurosciences, life sciences, and physics.

Handbook of Decision Support Systems for Neurological Disorders

Author : D. Jude Hemanth
Publisher : Academic Press
ISBN 13 : 0128222727
Total Pages : 322 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Handbook of Decision Support Systems for Neurological Disorders by : D. Jude Hemanth

Download or read book Handbook of Decision Support Systems for Neurological Disorders written by D. Jude Hemanth and published by Academic Press. This book was released on 2021-03-30 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided diagnosis systems for neurological disorders. While computer-aided decision support systems for different medical imaging modalities are available, this is the first book to solely concentrate on decision support systems for neurological disorders. Due to the increase in the prevalence of diseases such as Alzheimer, Parkinson's and Dementia, this book will have significant importance in the medical field. Topics discussed include recent computational approaches, different types of neurological disorders, deep convolution neural networks, generative adversarial networks, auto encoders, recurrent neural networks, and modified/hybrid artificial neural networks. - Includes applications of computer intelligence and decision support systems for the diagnosis and analysis of a variety of neurological disorders - Presents in-depth, technical coverage of computer-aided systems for tumor image classification, Alzheimer's disease detection, dementia detection using deep belief neural networks, and morphological approaches for stroke detection - Covers disease diagnosis for cerebral palsy using auto-encoder approaches, contrast enhancement for performance enhanced diagnosis systems, autism detection using fuzzy logic systems, and autism detection using generative adversarial networks - Written by engineers to help engineers, computer scientists, researchers and clinicians understand the technology and applications of decision support systems for neurological disorders

Artificial Intelligence for Neurological Disorders

Author : Ajith Abraham
Publisher : Academic Press
ISBN 13 : 0323902782
Total Pages : 434 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Artificial Intelligence for Neurological Disorders by : Ajith Abraham

Download or read book Artificial Intelligence for Neurological Disorders written by Ajith Abraham and published by Academic Press. This book was released on 2022-09-23 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation. The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances. - Discusses various AI and ML methods to apply for neurological research - Explores Deep Learning techniques for brain MRI images - Covers AI techniques for the early detection of neurological diseases and seizure prediction - Examines cognitive therapies using AI and Deep Learning methods

Artificial Intelligence in the Age of Neural Networks and Brain Computing

Author : Robert Kozma
Publisher : Academic Press
ISBN 13 : 0323958168
Total Pages : 398 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Artificial Intelligence in the Age of Neural Networks and Brain Computing by : Robert Kozma

Download or read book Artificial Intelligence in the Age of Neural Networks and Brain Computing written by Robert Kozma and published by Academic Press. This book was released on 2023-10-27 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks

Artificial intelligence-based computer-aided diagnosis applications for brain disorders from medical imaging data, volume II

Author : Ahmed Shalaby
Publisher : Frontiers Media SA
ISBN 13 : 2832531008
Total Pages : 121 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Artificial intelligence-based computer-aided diagnosis applications for brain disorders from medical imaging data, volume II by : Ahmed Shalaby

Download or read book Artificial intelligence-based computer-aided diagnosis applications for brain disorders from medical imaging data, volume II written by Ahmed Shalaby and published by Frontiers Media SA. This book was released on 2023-08-02 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence

Author : Anitha S. Pillai
Publisher : Academic Press
ISBN 13 : 0323886264
Total Pages : 356 pages
Book Rating : 4.3/5 (238 download)

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Book Synopsis Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence by : Anitha S. Pillai

Download or read book Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence written by Anitha S. Pillai and published by Academic Press. This book was released on 2022-02-23 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence focuses on how the neurosciences can benefit from advances in AI, especially in areas such as medical image analysis for the improved diagnosis of Alzheimer's disease, early detection of acute neurologic events, prediction of stroke, medical image segmentation for quantitative evaluation of neuroanatomy and vasculature, diagnosis of Alzheimer's Disease, autism spectrum disorder, and other key neurological disorders. Chapters also focus on how AI can help in predicting stroke recovery, and the use of Machine Learning and AI in personalizing stroke rehabilitation therapy. Other sections delve into Epilepsy and the use of Machine Learning techniques to detect epileptogenic lesions on MRIs and how to understand neural networks. - Provides readers with an understanding on the key applications of artificial intelligence and machine learning in the diagnosis and treatment of the most important neurological disorders - Integrates recent advancements of artificial intelligence and machine learning to the evaluation of large amounts of clinical data for the early detection of disorders such as Alzheimer's Disease, autism spectrum disorder, Multiple Sclerosis, headache disorder, Epilepsy, and stroke - Provides readers with illustrative examples of how artificial intelligence can be applied to outcome prediction, neurorehabilitation and clinical exams, including a wide range of case studies in predicting and classifying neurological disorders

Machine Learning and Deep Learning in Neuroimaging Data Analysis

Author : Anitha S. Pillai
Publisher : CRC Press
ISBN 13 : 1003815545
Total Pages : 133 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Machine Learning and Deep Learning in Neuroimaging Data Analysis by : Anitha S. Pillai

Download or read book Machine Learning and Deep Learning in Neuroimaging Data Analysis written by Anitha S. Pillai and published by CRC Press. This book was released on 2024-02-15 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) and deep learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in identifying, classifying or predicting neurological problems that require neuroimaging. With the ability to model high-dimensional datasets, supervised learning algorithms can help in relating brain images to behavioral or clinical observations and unsupervised learning can uncover hidden structures/patterns in images. Bringing together artificial intelligence (AI) experts as well as medical practitioners, these chapters cover the majority of neuro problems that use neuroimaging for diagnosis, along with case studies and directions for future research.

Machine Learning for Medical Image Reconstruction

Author : Farah Deeba
Publisher : Springer Nature
ISBN 13 : 3030615987
Total Pages : 170 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Machine Learning for Medical Image Reconstruction by : Farah Deeba

Download or read book Machine Learning for Medical Image Reconstruction written by Farah Deeba and published by Springer Nature. This book was released on 2020-10-21 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Intelligent Data Analysis

Author : Deepak Gupta
Publisher : John Wiley & Sons
ISBN 13 : 1119544459
Total Pages : 428 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Intelligent Data Analysis by : Deepak Gupta

Download or read book Intelligent Data Analysis written by Deepak Gupta and published by John Wiley & Sons. This book was released on 2020-07-13 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis tools.

Deep Learning for Neurological Disorders in Children

Author : Saman Sargolzaei
Publisher : Frontiers Media SA
ISBN 13 : 2832508480
Total Pages : 134 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Deep Learning for Neurological Disorders in Children by : Saman Sargolzaei

Download or read book Deep Learning for Neurological Disorders in Children written by Saman Sargolzaei and published by Frontiers Media SA. This book was released on 2022-12-02 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Bio-Inspired Algorithms and Devices for Treatment of Cognitive Diseases Using Future Technologies

Author : Gupta, Shweta
Publisher : IGI Global
ISBN 13 : 179989536X
Total Pages : 309 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Bio-Inspired Algorithms and Devices for Treatment of Cognitive Diseases Using Future Technologies by : Gupta, Shweta

Download or read book Bio-Inspired Algorithms and Devices for Treatment of Cognitive Diseases Using Future Technologies written by Gupta, Shweta and published by IGI Global. This book was released on 2022-02-11 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: As there are no proper medical tests available to predict certain diseases such as Alzheimer’s and Parkinson’s at an early stage, there is a need to further study and consider the potential uses of bio- and nature-inspired algorithms and future technologies such as machine learning in correlation to disease detection and treatment. Bio-Inspired Algorithms and Devices for Treatment of Cognitive Diseases Using Future Technologies considers new tools for early detection of cognitive brain diseases using devices and algorithms whose basic concept is taken from nature and discusses design, analysis, and application of various bionics or bio-inspired algorithms. Covering topics such as depression and cognitive science, this publication is an ideal resource for researchers, academicians, industry professionals, psychologists, psychiatrists, nurses, engineers, instructors, and students.

High Performance and Machine Learning Algorithms for Brain FMRI Data

Author : Taban Eslami
Publisher :
ISBN 13 :
Total Pages : 118 pages
Book Rating : 4.:/5 (124 download)

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Book Synopsis High Performance and Machine Learning Algorithms for Brain FMRI Data by : Taban Eslami

Download or read book High Performance and Machine Learning Algorithms for Brain FMRI Data written by Taban Eslami and published by . This book was released on 2020 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain disorders are very difficult to diagnose for reasons such as overlapping nature of symptoms, individual differences in brain structure, lack of medical tests and unknown causes of some disorders. The current psychiatric diagnostic process is based on behavioral observation and may be prone to misdiagnosis. Noninvasive brain imaging technologies such as Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) make the process of understanding the structure and function of the brain easier. Quantitative analysis of brain imaging data using machine learning and data mining techniques can be advantageous not only to increase the accuracy of brain disorder diagnosis but also to unravel unknown facts about the complex function of the brain. Research studies have shown functional connectivities of brain contain discriminative patterns that are widely used in a variety of studies such as fMRI classification. In this dissertation, we designed machine learning and deep learning models for diagnosing brain disorders such as ADHD and ASD using fMRI data. In order to reduce the risk of overfitting in deep learning methods, we proposed a data augmentation approach for generating artificial samples from available data. Our models are able to improve the accuracy of classifying healthy samples from patients up to 28%. comparing to state-of-the-art solutions. Analysis of fMRI data considering a huge number of voxels (smallest addressable element of fMRI data) is very time-consuming. One example is computing pairwise functional connections between voxels using measures like Pearson’s correlation. To tackle this issue, we designed two GPU based frameworks based on matrix multiplication for computing pairwise correlations that deliver around 3 times speedup against state-of-the-art GPU based methods. We expanded these frameworks to compute dynamic functional connectivity which involves computing multiple sets of pairwise correlations each associated with specific time windows in original time series followed by designing two methodologies for reducing the space requirements of pairwise correlations.

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning

Author : Rani, Geeta
Publisher : IGI Global
ISBN 13 : 1799827437
Total Pages : 586 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning by : Rani, Geeta

Download or read book Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning written by Rani, Geeta and published by IGI Global. This book was released on 2020-10-16 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Signal Processing and Machine Learning for Brain-Machine Interfaces

Author : Toshihisa Tanaka
Publisher : Institution of Engineering and Technology
ISBN 13 : 1785613987
Total Pages : 355 pages
Book Rating : 4.7/5 (856 download)

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Book Synopsis Signal Processing and Machine Learning for Brain-Machine Interfaces by : Toshihisa Tanaka

Download or read book Signal Processing and Machine Learning for Brain-Machine Interfaces written by Toshihisa Tanaka and published by Institution of Engineering and Technology. This book was released on 2018-09-13 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain-machine interfacing or brain-computer interfacing (BMI/BCI) is an emerging and challenging technology used in engineering and neuroscience. The ultimate goal is to provide a pathway from the brain to the external world via mapping, assisting, augmenting or repairing human cognitive or sensory-motor functions.

Applications of Deep Learning and Big IoT on Personalized Healthcare Services

Author : Wason, Ritika
Publisher : IGI Global
ISBN 13 : 1799821021
Total Pages : 248 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Applications of Deep Learning and Big IoT on Personalized Healthcare Services by : Wason, Ritika

Download or read book Applications of Deep Learning and Big IoT on Personalized Healthcare Services written by Wason, Ritika and published by IGI Global. This book was released on 2020-02-07 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Healthcare is an industry that has seen great advancements in personalized services through big data analytics. Despite the application of smart devices in the medical field, the mass volume of data that is being generated makes it challenging to correctly diagnose patients. This has led to the implementation of precise algorithms that can manage large amounts of information and successfully use smart living in medical environments. Professionals worldwide need relevant research on how to successfully implement these smart technologies within their own personalized healthcare processes. Applications of Deep Learning and Big IoT on Personalized Healthcare Services is a pivotal reference source that provides a collection of innovative research on the analytical methods and applications of smart algorithms for the personalized treatment of patients. While highlighting topics including cognitive computing, natural language processing, and supply chain optimization, this book is ideally designed for network designers, analysts, technology specialists, medical professionals, developers, researchers, academicians, and post-graduate students seeking relevant information on smart developments within individualized healthcare.