Deep Learning Approaches In Image Guided Diagnosis For Tumors
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Deep Learning Approaches In Image Guided Diagnosis For Tumors
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Author : Shahid Mumtaz
language : en
Publisher: Frontiers Media SA
Release Date : 2023-03-13
Deep Learning Approaches In Image Guided Diagnosis For Tumors written by Shahid Mumtaz and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-13 with Medical categories.
Deep Learning For Cancer Diagnosis
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Author : Utku Kose
language : en
Publisher: Springer Nature
Release Date : 2020-09-12
Deep Learning For Cancer Diagnosis written by Utku Kose and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-12 with Technology & Engineering categories.
This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.
Advanced Machine Learning Approaches In Cancer Prognosis
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Author : Janmenjoy Nayak
language : en
Publisher: Springer Nature
Release Date : 2021-05-29
Advanced Machine Learning Approaches In Cancer Prognosis written by Janmenjoy Nayak and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-29 with Technology & Engineering categories.
This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.
Brain Tumor Mri Image Segmentation Using Deep Learning Techniques
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Author : Jyotismita Chaki
language : en
Publisher: Academic Press
Release Date : 2021-11-27
Brain Tumor Mri Image Segmentation Using Deep Learning Techniques written by Jyotismita Chaki and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-27 with Science categories.
Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation. After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties. A focus is placed on the application of different types of convolutional neural networks, like single path, multi path, fully convolutional network, cascade convolutional neural networks, Long Short-Term Memory - Recurrent Neural Network and Gated Recurrent Units, and more. The book also highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in brain tumor segmentation. Provides readers with an understanding of deep learning-based approaches in the field of brain tumor segmentation, including preprocessing techniques Integrates recent advancements in the field, including the transformation of low-resolution brain tumor images into super-resolution images using deep learning-based methods, single path Convolutional Neural Network based brain tumor segmentation, and much more Includes coverage of Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN), Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN), Generative Adversarial Networks (GAN), Auto Encoder based brain tumor segmentation, and Ensemble deep learning Model based brain tumor segmentation Covers research Issues and the future of deep learning-based brain tumor segmentation
Current Applications Of Deep Learning In Cancer Diagnostics
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Author : Jyotismita Chaki
language : en
Publisher: CRC Press
Release Date : 2023-02-22
Current Applications Of Deep Learning In Cancer Diagnostics written by Jyotismita Chaki and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-22 with Computers categories.
This book examines deep learning-based approaches in the field of cancer diagnostics, as well as pre-processing techniques, which are essential to cancer diagnostics. Topics include introduction to current applications of deep learning in cancer diagnostics, pre-processing of cancer data using deep learning, review of deep learning techniques in oncology, overview of advanced deep learning techniques in cancer diagnostics, prediction of cancer susceptibility using deep learning techniques, prediction of cancer reoccurrence using deep learning techniques, deep learning techniques to predict the grading of human cancer, different human cancer detection using deep learning techniques, prediction of cancer survival using deep learning techniques, complexity in the use of deep learning in cancer diagnostics, and challenges and future scopes of deep learning techniques in oncology.
Machine Learning And Deep Learning Techniques For Medical Image Recognition
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Author : Ben Othman Soufiene
language : en
Publisher: CRC Press
Release Date : 2023-12-01
Machine Learning And Deep Learning Techniques For Medical Image Recognition written by Ben Othman Soufiene and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-01 with Technology & Engineering categories.
Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.
Deep Learning And Convolutional Neural Networks For Medical Imaging And Clinical Informatics
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Author : Le Lu
language : en
Publisher: Springer Nature
Release Date : 2019-09-19
Deep Learning And Convolutional Neural Networks For Medical Imaging And Clinical Informatics written by Le Lu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-19 with Computers categories.
This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval. The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
Machine And Deep Learning In Oncology Medical Physics And Radiology
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Author : Issam El Naqa
language : en
Publisher: Springer Nature
Release Date : 2022-02-02
Machine And Deep Learning In Oncology Medical Physics And Radiology written by Issam El Naqa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Science categories.
This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Application Of Deep Learning Methods In Healthcare And Medical Science
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Author : Rohit Tanwar
language : en
Publisher: CRC Press
Release Date : 2023-01-12
Application Of Deep Learning Methods In Healthcare And Medical Science written by Rohit Tanwar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-12 with Computers categories.
The volume provides a wealth of up-to-date information on developments and applications of deep learning in healthcare and medicine, providing deep insight and understanding of novel applications that address the tough questions of disease diagnosis, prevention, and immunization. The volume looks at applications of deep learning for major medical challenges such as cancer detection and identification, birth asphyxia among neonates, kidney abnormalities, white blood cell segmentation, diabetic retinopathy detection, and Covid-19 diagnosis, prevention, and immunization. The volume discusses applications of deep learning in detection, diagnosis, intensive examination and evaluation, genomic sequencing, convolutional neural networks for image recognition and processing, and more for health issues such as kidney problems, brain tumors, lung damage, and breast cancer. The authors look at ML for brain tumor segmentation, in lung CT scans, in digital X-ray devices, and for logistic and transport systems for effective delivery of healthcare.
Medical Imaging
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Author : K.C. Santosh
language : en
Publisher: CRC Press
Release Date : 2019-08-20
Medical Imaging written by K.C. Santosh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-20 with Computers categories.
The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.