Artificial Intelligence And Machine Learning In 2d 3d Medical Image Processing
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Artificial Intelligence And Machine Learning In 2d 3d Medical Image Processing
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Author : Rohit Raja
language : en
Publisher: CRC Press
Release Date : 2020-12-22
Artificial Intelligence And Machine Learning In 2d 3d Medical Image Processing written by Rohit Raja and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-22 with Medical categories.
Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management. Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology. This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems. This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning. FEATURES Highlights the framework of robust and novel methods for medical image processing techniques Discusses implementation strategies and future research directions for the design and application requirements of medical imaging Examines real-time application needs Explores existing and emerging image challenges and opportunities in the medical field
Artificial Intelligence And Machine Learning In 2d 3d Medical Image Processing
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Author : Rohit Raja
language : en
Publisher: CRC Press
Release Date : 2020-12-23
Artificial Intelligence And Machine Learning In 2d 3d Medical Image Processing written by Rohit Raja and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-23 with Technology & Engineering categories.
Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management. Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology. This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems. This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning. FEATURES Highlights the framework of robust and novel methods for medical image processing techniques Discusses implementation strategies and future research directions for the design and application requirements of medical imaging Examines real-time application needs Explores existing and emerging image challenges and opportunities in the medical field
Applications Of Artificial Intelligence In Medical Imaging
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Author : Abdulhamit Subasi
language : en
Publisher: Academic Press
Release Date : 2022-11-10
Applications Of Artificial Intelligence In Medical Imaging written by Abdulhamit Subasi and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-10 with Science categories.
Applications of Artificial Intelligence in Medical Imaging provides the description of various biomedical image analysis in disease detection using AI that can be used to incorporate knowledge obtained from different medical imaging devices such as CT, X-ray, PET and ultrasound. The book discusses the use of AI for detection of several cancer types, including brain tumor, breast, pancreatic, rectal, lung colon, and skin. In addition, it explains how AI and deep learning techniques can be used to diagnose Alzheimer's, Parkinson's, COVID-19 and mental conditions. This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about AI and its impact in medical/biomedical image analysis. - Discusses new deep learning algorithms for image analysis and how they are used for medical images - Provides several examples for each imaging technique, along with their application areas so that readers can rely on them as a clinical decision support system - Describes how new AI tools may contribute significantly to the successful enhancement of a single patient's clinical knowledge to improve treatment outcomes
Artificial Intelligence In Medical Imaging
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Author : Erik R. Ranschaert
language : en
Publisher: Springer
Release Date : 2019-01-29
Artificial Intelligence In Medical Imaging written by Erik R. Ranschaert and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-29 with Medical categories.
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implicationsfor radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Applications Of Artificial Intelligence In Healthcare And Biomedicine
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Author : Abdulhamit Subasi
language : en
Publisher: Elsevier
Release Date : 2024-03-10
Applications Of Artificial Intelligence In Healthcare And Biomedicine written by Abdulhamit Subasi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-10 with Science categories.
Applications of Artificial Intelligence in Healthcare and Biomedicine provides updated knowledge on the applications of artificial intelligence in medical image analysis. The book starts with an introduction to Artificial Intelligence techniques for Healthcare and Biomedicine. In 16 chapters it presents artificial applications in Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyography (EMG), signal analysis, Computed Tomography (CT), Magnetic Resonance Imaging (MR) and Ultrasound image analysis. It equips researchers with tools for early breast cancer detection from mammograms using artificial intelligence (AI), AI models to detect lung cancer using histopathological images and a deep learning-based approach to get a proper and faster diagnosis of the Optical Coherence Tomography (OCT) images. It also presents present 3D medical image analysis using 3D Convolutional Neural Networks (CNNs). Applications of Artificial Intelligence in Healthcare and Biomedicine closes with a chapter on AI-based approach to forecast diabetes patients' hospital re-admissions. This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about the applications of Artificial Intelligence and its impact in medical/biomedical image analysis. - Provides knowledge on Artificial Intelligence algorithms for clinical data analysis - Gives insights into both AI applications in biomedical signal analysis, biomedical image analysis, and applications in healthcare, including drug discovery - Equips researchers with tools for early breast cancer detection
Deep Learning For Medical Image Analysis
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Author : S. Kevin Zhou
language : en
Publisher: Academic Press
Release Date : 2017-01-18
Deep Learning For Medical Image Analysis written by S. Kevin Zhou and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-18 with Computers categories.
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
Optimized Predictive Models In Health Care Using Machine Learning
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Author : Sandeep Kumar
language : en
Publisher: John Wiley & Sons
Release Date : 2024-02-08
Optimized Predictive Models In Health Care Using Machine Learning written by Sandeep Kumar 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 2024-02-08 with Computers categories.
OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application; emphasizes validating and evaluating predictive models; provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare; highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.
Introduction To Biomedical Imaging
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Author : Andrew Webb
language : en
Publisher: John Wiley & Sons
Release Date : 2022-10-25
Introduction To Biomedical Imaging written by Andrew Webb 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 2022-10-25 with Science categories.
Introduction to BiomedicalImaging A state-of-the-art exploration of the foundations and latest developments in biomedical imaging technology In the newly revised second edition of Introduction to Biomedical Imaging, distinguished researcher Dr. Andrew Webb delivers a comprehensive description of the fundamentals and applications of the most important current medical imaging techniques: X-ray and computed tomography, nuclear medicine, ultrasound, magnetic resonance imaging, and various optical-based methods. Each chapter explains the physical principles, instrument design, data acquisition, image reconstruction, and clinical applications of its respective modality. This latest edition incorporates descriptions of recent developments in photon counting CT, total body PET, superresolution-based ultrasound, phased-array MRI technology, optical coherence tomography, and iterative and model-based image reconstruction techniques. The final chapter discusses the increasing role of artificial intelligence/deep learning in biomedical imaging. The text also includes a thorough introduction to general image characteristics, including discussions of signal-to-noise and contrast-to-noise. Perfect for graduate and senior undergraduate students of biomedical engineering, Introduction to Biomedical Imaging, 2nd Edition will also earn a place in the libraries of medical imaging professionals with an interest in medical imaging techniques.
Genomics At The Nexus Of Ai Computer Vision And Machine Learning
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Author : Shilpa Choudhary
language : en
Publisher: John Wiley & Sons
Release Date : 2024-10-01
Genomics At The Nexus Of Ai Computer Vision And Machine Learning written by Shilpa Choudhary 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 2024-10-01 with Computers categories.
The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations. The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. Audience The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies.
Artificial Intelligence For Smart Healthcare
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Author : Parul Agarwal
language : en
Publisher: Springer Nature
Release Date : 2023-06-09
Artificial Intelligence For Smart Healthcare written by Parul Agarwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-09 with Technology & Engineering categories.
This book provides information on interdependencies of medicine and telecommunications engineering and how the two must rely on each other to effectively function in this era. The book discusses new techniques for medical service improvisation such as clear-cut views on medical technologies. The authors provide chapters on communication essentiality in healthcare, processing of medical amenities using medical images, the importance of data and information technology in medicine, and machine learning and artificial intelligence in healthcare. Authors include researchers, academics, and professionals in the field.