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Deep Learning For Neurological Disorders In Children 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 Deep Learning For Neurological Disorders In Children book. This book definitely worth reading, it is an incredibly well-written.
Author : Saman Sargolzaei,Ali Fatemi,Fahimeh Mamashli,Anna Farzindar,Yalda Mohsenzadeh,Arman Sargolzaei
Publisher : Frontiers Media SA
Page : 134 pages
File Size : 48,8 Mb
Release : 2022-12-02
Category : Science
ISBN : 9782832508480
Author : Paul, Sudip,Bhattacharya, Pallab,Bit, Arindam
Publisher : IGI Global
Page : 376 pages
File Size : 46,9 Mb
Release : 2019-06-28
Category : Medical
ISBN : 9781522585688
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.
Author : Jyotismita Chaki
Publisher : CRC Press
Page : 268 pages
File Size : 44,8 Mb
Release : 2023-05-15
Category : Computers
ISBN : 9781000872187
This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders. It includes application of feed-forward neural networks, deep generative models, convolutional neural networks, graph convolutional networks, and recurrent neural networks in the field of diagnosis of neurological disorders. Along with this, data preprocessing including scaling, correction, trimming, and normalization is also included. Offers a detailed description of the deep learning approaches used for the diagnosis of neurological disorders. Demonstrates concepts of deep learning algorithms using diagrams, data tables, and examples for the diagnosis of neurodegenerative, neurodevelopmental, and psychiatric disorders. Helps build, train, and deploy different types of deep architectures for diagnosis. Explores data preprocessing techniques involved in diagnosis. Includes real-time case studies and examples. This book is aimed at graduate students and researchers in biomedical imaging and machine learning.
Author : Jyotismita Chaki
Publisher : CRC Press
Page : 237 pages
File Size : 44,5 Mb
Release : 2023-05-15
Category : Computers
ISBN : 9781000872170
This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders. It includes application of feed-forward neural networks, deep generative models, convolutional neural networks, graph convolutional networks, and recurrent neural networks in the field of diagnosis of neurological disorders. Along with this, data preprocessing including scaling, correction, trimming, and normalization is also included. Offers a detailed description of the deep learning approaches used for the diagnosis of neurological disorders. Demonstrates concepts of deep learning algorithms using diagrams, data tables, and examples for the diagnosis of neurodegenerative, neurodevelopmental, and psychiatric disorders. Helps build, train, and deploy different types of deep architectures for diagnosis. Explores data preprocessing techniques involved in diagnosis. Includes real-time case studies and examples. This book is aimed at graduate students and researchers in biomedical imaging and machine learning.
Author : Ajith Abraham,Sujata Dash,Subhendu Kumar Pani,Laura García-Hernández
Publisher : Academic Press
Page : 434 pages
File Size : 41,8 Mb
Release : 2022-09-23
Category : Medical
ISBN : 9780323902786
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
Author : Anitha S. Pillai,Bindu Menon
Publisher : CRC Press
Page : 133 pages
File Size : 46,6 Mb
Release : 2024-02-15
Category : Computers
ISBN : 9781003815549
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.
Author : D. Jude Hemanth
Publisher : Elsevier
Page : 304 pages
File Size : 52,8 Mb
Release : 2023-11-17
Category : Science
ISBN : 9780443137730
Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications explores the different possibilities of providing AI based neuro-rehabilitation methods to treat neurological disorders. This book provides in-depth knowledge on the challenges and solutions associated with the different varieties of neuro-rehabilitation through the inclusion of case studies and real-time scenarios in different geographical locations. Beginning with an overview of neuro-rehabilitation applications, the book discusses the role of machine learning methods in brain function grading for adults with Mild Cognitive Impairment, Brain Computer Interface for post-stroke patients, developing assistive devices for paralytic patients, and cognitive treatment for spinal cord injuries. Topics also include AI-based video games to improve the brain performances in children with autism and ADHD, deep learning approaches and magnetoencephalography data for limb movement, EEG signal analysis, smart sensors, and the application of robotic concepts for gait control. Incorporates artificial intelligence techniques into neuro-rehabilitation and presents novel ideas for this process Provides in-depth case studies and state-of-the-art methods, along with the experimental study Presents a block diagram based complete set-up in each chapter to help in real-time implementation
Author : Hemanth D. Jude
Publisher : Academic Press
Page : 320 pages
File Size : 47,8 Mb
Release : 2021-03-30
Category : Science
ISBN : 9780128222720
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
Author : Anitha S. Pillai,Bindu Menon
Publisher : Academic Press
Page : 356 pages
File Size : 48,5 Mb
Release : 2022-02-23
Category : Science
ISBN : 9780323886260
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
Author : Andrea Mechelli,Sandra Vieira
Publisher : Academic Press
Page : 412 pages
File Size : 45,5 Mb
Release : 2019-11-14
Category : Medical
ISBN : 9780128157404
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
Author : Olivier Colliot
Publisher : Springer Nature
Page : 1058 pages
File Size : 51,8 Mb
Release : 2023-07-24
Category : Medical
ISBN : 9781071631959
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.
Author : Tanu Wadhera,Deepti Kakkar
Publisher : Taylor & Francis
Page : 273 pages
File Size : 48,8 Mb
Release : 2022-04-20
Category : Medical
ISBN : 9781000536034
This cutting-edge volume explores how technological tools can be designed, engineered and implemented to assess and support individuals with neurodevelopmental disorders from diagnosis through to rehabilitation. Tanu Wadhera and Deepti Kakkar and their expert contributors focus on technological tools as equalizers in Neurodevelopmental disorders (NDDs) at every stage, the importance of demand-specific design, and how we can best engineer and deploy both invasive and non-invasive individual-centered approaches that support and connect individuals. Considering the perspectives of patients, clinicians and technologists, it explores key topics including design and evaluation of platforms for tech-tools, automated diagnosis, brain imaging techniques, tech-diagnostic frameworks with AI and machine learning, sensing technology, smart brain prosthetics, gamification, alternative communication devices, and education tools and interactive toys. Outlining future challenges for research, Enabling Technology for Neurodevelopmental Disorders is useful for scholars and professionals in psychology, technology, engineering and medicine concerned with design, development and evaluation of a range of assistive technological tools.
Author : M. Murugappan,Yuvaraj Rajamanickam
Publisher : Springer Nature
Page : 295 pages
File Size : 43,8 Mb
Release : 2022-06-17
Category : Technology & Engineering
ISBN : 9783030978457
Biomedical signals provide unprecedented insight into abnormal or anomalous neurological conditions. The computer-aided diagnosis (CAD) system plays a key role in detecting neurological abnormalities and improving diagnosis and treatment consistency in medicine. This book covers different aspects of biomedical signals-based systems used in the automatic detection/identification of neurological disorders. Several biomedical signals are introduced and analyzed, including electroencephalogram (EEG), electrocardiogram (ECG), heart rate (HR), magnetoencephalogram (MEG), and electromyogram (EMG). It explains the role of the CAD system in processing biomedical signals and the application to neurological disorder diagnosis. The book provides the basics of biomedical signal processing, optimization methods, and machine learning/deep learning techniques used in designing CAD systems for neurological disorders.
Author : Salam Salloum-Asfar,Javier Corral,Jesualdo Tomás Fernández-Breis,Nasser H. Zawia
Publisher : Frontiers Media SA
Page : 255 pages
File Size : 54,9 Mb
Release : 2023-07-24
Category : Science
ISBN : 9782832529942
Author : Ayman S. El-Baz,Institute of Physics (Gran Bretanya),Jasjit S. Suri
Publisher : Unknown
Page : 0 pages
File Size : 52,7 Mb
Release : 2019
Category : Attention-deficit disorder in adults
ISBN : 0750318228
Attention deficit hyperactivity disorder (ADHD) is a common neurological disorder that impacts focus, self-control, and other skills important in daily life. Caused by differences in brain anatomy and wiring, it is known to be one of the most common conditions in childhood. As ADHD plays a serious role in how children function in school and in their everyday life, having a deep understanding of this neurological condition is critical. This book explores recent advances in neuroimaging techniques, methods, applications and machine learning algorithms.
