Brain Tumor Mri Image Segmentation Using Deep Learning Techniques
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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
Deep Neural Networks For Multimodal Imaging And Biomedical Applications
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Author : Suresh, Annamalai
language : en
Publisher: IGI Global
Release Date : 2020-06-26
Deep Neural Networks For Multimodal Imaging And Biomedical Applications written by Suresh, Annamalai and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-26 with Computers categories.
The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.
Imaging Of Brain Tumors With Histological Correlations
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Author : Antonios Drevelegas
language : en
Publisher: Springer Science & Business Media
Release Date : 2002-07-10
Imaging Of Brain Tumors With Histological Correlations written by Antonios Drevelegas and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-07-10 with Medical categories.
This volume provides a thorough treatment of the diagnosis of brain tumors by correlating radiographic image features to the underlying pathology. Theoretical considerations and illustrations depicting common and uncommon imaging characteristics of various brain tumors are presented. All modern imaging modalities are used to complete a diagnostic overview of brain tumors with emphasis on recent advances in diagnostic neuroradiology. The book has been designed as a clinical tool for radiologists and other clinicians interested in the current diagnostic approach to brain tumors.
Deep Learning And Data Labeling For Medical Applications
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Author : Gustavo Carneiro
language : en
Publisher: Springer
Release Date : 2016-10-07
Deep Learning And Data Labeling For Medical Applications written by Gustavo Carneiro and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-07 with Computers categories.
This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.
Brain Tumor Detection Based On Convolutional Neural Network With Neutrosophic Expert Maximum Fuzzy Sure Entropy
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Author : Fatih ÖZYURT
language : en
Publisher: Infinite Study
Release Date :
Brain Tumor Detection Based On Convolutional Neural Network With Neutrosophic Expert Maximum Fuzzy Sure Entropy written by Fatih ÖZYURT and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Brain tumor classification is a challenging task in the field of medical image processing. The present study proposes a hybrid method using Neutrosophy and Convolutional Neural Network (NS-CNN). It aims to classify tumor region areas that are segmented from brain images as benign and malignant. In the first stage, MRI images were segmented using the neutrosophic set – expert maximum fuzzy-sure entropy (NS-EMFSE) approach.
Computational Analysis And Deep Learning For Medical Care
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Author : Amit Kumar Tyagi
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-24
Computational Analysis And Deep Learning For Medical Care written by Amit Kumar Tyagi 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 2021-08-24 with Computers categories.
The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems. We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.
Machine Learning For Sustainable Development
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Author : Kamal Kant Hiran
language : en
Publisher: de Gruyter
Release Date : 2021-08-09
Machine Learning For Sustainable Development written by Kamal Kant Hiran and has been published by de Gruyter this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-09 with categories.
THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research findings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the field and create a broad knowledge about the most recent research. Series Editor Dr. Siddhartha Bhattacharyya, CHRIST (Deemed to be University), Bangalore, India Editorial Advisory Board Dr. Elizabeth Behrman, Wichita State University, Kansas, USA Dr. Goran Klepac Dr. Leo Mrsic, Algebra University College, Croatia Dr. Aboul Ella Hassanien, Cairo University, Egypt Dr. Jan Platos, VSB-Technical University of Ostrava, Czech Republic Dr. Xiao-Zhi Gao, University of Eastern Finland, Finland Dr. Wellington Pinheiro dos Santos, Federal University of Pernambuco, Brazil
Machine Learning And Deep Learning In Real Time Applications
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Author : Mahrishi, Mehul
language : en
Publisher: IGI Global
Release Date : 2020-04-24
Machine Learning And Deep Learning In Real Time Applications written by Mahrishi, Mehul and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-24 with Computers categories.
Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.
Early Prediction Of Diseases Using Deep Learning And Machine Learning Techniques
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Author : Dr. Sasidhar B
language : en
Publisher: Archers & Elevators Publishing House
Release Date :
Early Prediction Of Diseases Using Deep Learning And Machine Learning Techniques written by Dr. Sasidhar B and has been published by Archers & Elevators Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on with Antiques & Collectibles categories.