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.
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
Machine Learning In Radiation Oncology
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Author : Issam El Naqa
language : en
Publisher: Springer
Release Date : 2015-06-19
Machine Learning In Radiation Oncology written by Issam El Naqa and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-19 with Medical categories.
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Deep Learning For Smart Healthcare
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Author : K. Murugeswari
language : en
Publisher: CRC Press
Release Date : 2024-05-15
Deep Learning For Smart Healthcare written by K. Murugeswari and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-15 with Medical categories.
Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data. Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process. Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.
Image Guided Hypofractionated Stereotactic Radiosurgery
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Author : Arjun Sahgal
language : en
Publisher: CRC Press
Release Date : 2021-07-15
Image Guided Hypofractionated Stereotactic Radiosurgery written by Arjun Sahgal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-15 with Medical categories.
Following recent developments in hypofractionated stereotactic radiation therapy (SRT) for brain and spine tumors, this new edition offers a fully updated and comprehensive "how-to" guidance on hypofractionated SRT for brain and spine metastases, glioma, benign tumors, and other tumor types. Presenting the state of the art of the technology and practice, this book: • Discusses the pros and cons of hypofractionated SRT compared to single-fraction radiosurgery, providing a deeper understanding of radiosurgery and radiobiology • Explains the toxicity and adverse effects of hypofractionated SRT including the dosage of 24 Gy in two spine SBRT fractionation schemes, aiding practitioners in communicating the risks and benefits of treatment and in obtaining consent from their patients • Outlines the current standards for safe practice, including checklists for implementation • Explores new technologies for brain and spine tumors including LITT, MR-guided focused ultrasound, and Zap technology, with chapters authored by well-recognized experts in the radiation, oncology, and neurosurgery communities; this book delivers a level of technological and clinical detail not available in journal papers This book is suitable for radiation oncologists, neurosurgeons, and medical physicists who specialize in brain and/or spine radiosurgery or want to start a program and need a comprehensive reference with key checklists for practice.
Advanced Machine Vision Paradigms For Medical Image Analysis
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Author : Tapan K. Gandhi
language : en
Publisher: Academic Press
Release Date : 2020-08-11
Advanced Machine Vision Paradigms For Medical Image Analysis written by Tapan K. Gandhi and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-11 with Computers categories.
Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs. - Explores major emerging trends in technology which are supporting the current advancement of medical image analysis with the help of computational intelligence - Highlights the advancement of conventional approaches in the field of medical image processing - Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques, as well as their applications in medical image analysis
Deep Learning In Biomedical Signal And Medical Imaging
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Author : Ngangbam Herojit Singh
language : en
Publisher: CRC Press
Release Date : 2024-09-30
Deep Learning In Biomedical Signal And Medical Imaging written by Ngangbam Herojit Singh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-30 with Computers categories.
This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives. Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimer’s, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the reader’s head. It presents the results of the latest investigations in the field of Deep CNN 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 the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN 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 is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics.
Multimodal Biomedical Imaging Techniques
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Author : Nandakumar Kalarikkal
language : en
Publisher: Springer Nature
Release Date : 2025-03-08
Multimodal Biomedical Imaging Techniques written by Nandakumar Kalarikkal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-08 with Science categories.
This book highlights various aspects of multimodal imaging techniques. Innovations and progress in the field of advanced molecular imaging techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT), Fluorescence Imaging, Photoacoustic imaging(PAI), Fluorescence Molecular Tomography (FMT), Ultrasound (US), etc., are covered in this book. This book is an invaluable reference for students, professionals, and research scholars (primarily in the field of materials science, biomedical imaging, and nanoscience and nanotechnology) and also for those who want to nurture their scientific temper/skills in these areas.
Machine Learning Under Resource Constraints Applications
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Author : Katharina Morik
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2022-12-31
Machine Learning Under Resource Constraints Applications written by Katharina Morik and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-31 with Science categories.
Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel.