Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

Author: Jyotismita Chaki

Publisher: Academic Press

Published: 2021-11-27

Total Pages: 260

ISBN-13: 0323983952

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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


Book Synopsis Brain Tumor MRI Image Segmentation Using Deep Learning Techniques by : Jyotismita Chaki

Download or read book Brain Tumor MRI Image Segmentation Using Deep Learning Techniques written by Jyotismita Chaki and published by Academic Press. This book was released on 2021-11-27 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

Author: Jyotismita Chaki

Publisher: Elsevier

Published: 2021-12-02

Total Pages: 258

ISBN-13: 0323911714

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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


Book Synopsis Brain Tumor MRI Image Segmentation Using Deep Learning Techniques by : Jyotismita Chaki

Download or read book Brain Tumor MRI Image Segmentation Using Deep Learning Techniques written by Jyotismita Chaki and published by Elsevier. This book was released on 2021-12-02 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Deep Neural Networks for Multimodal Imaging and Biomedical Applications

Author: Suresh, Annamalai

Publisher: IGI Global

Published: 2020-06-26

Total Pages: 294

ISBN-13: 1799835928

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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.


Book Synopsis Deep Neural Networks for Multimodal Imaging and Biomedical Applications by : Suresh, Annamalai

Download or read book Deep Neural Networks for Multimodal Imaging and Biomedical Applications written by Suresh, Annamalai and published by IGI Global. This book was released on 2020-06-26 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Brain Tumor Detection Based on Convolutional Neural Network with Neutrosophic Expert Maximum Fuzzy Sure Entropy

Brain Tumor Detection Based on Convolutional Neural Network with Neutrosophic Expert Maximum Fuzzy Sure Entropy

Author: Fatih ÖZYURT

Publisher: Infinite Study

Published:

Total Pages: 16

ISBN-13:

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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.


Book Synopsis Brain Tumor Detection Based on Convolutional Neural Network with Neutrosophic Expert Maximum Fuzzy Sure Entropy by : Fatih ÖZYURT

Download or read book Brain Tumor Detection Based on Convolutional Neural Network with Neutrosophic Expert Maximum Fuzzy Sure Entropy written by Fatih ÖZYURT and published by Infinite Study. This book was released on with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Early Prediction of Diseases using Deep Learning and Machine Learning Techniques

Early Prediction of Diseases using Deep Learning and Machine Learning Techniques

Author: Dr. Sasidhar B

Publisher: Archers & Elevators Publishing House

Published:

Total Pages: 85

ISBN-13: 8119385497

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Book Synopsis Early Prediction of Diseases using Deep Learning and Machine Learning Techniques by : Dr. Sasidhar B

Download or read book Early Prediction of Diseases using Deep Learning and Machine Learning Techniques written by Dr. Sasidhar B and published by Archers & Elevators Publishing House. This book was released on with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Deep Learning and Data Labeling for Medical Applications

Deep Learning and Data Labeling for Medical Applications

Author: Gustavo Carneiro

Publisher: Springer

Published: 2016-10-07

Total Pages: 289

ISBN-13: 3319469762

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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.


Book Synopsis Deep Learning and Data Labeling for Medical Applications by : Gustavo Carneiro

Download or read book Deep Learning and Data Labeling for Medical Applications written by Gustavo Carneiro and published by Springer. This book was released on 2016-10-07 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Classification Using Convolutional Neural Network with Neutrosophy, Super-Resolution and SVM

Brain Tumor Classification Using Convolutional Neural Network with Neutrosophy, Super-Resolution and SVM

Author: Mubashir Tariq

Publisher: Infinite Study

Published: 2022-01-01

Total Pages: 24

ISBN-13:

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In the domain of Medical Image Analysis (MIA), it is difficult to perform brain tumor classification. With the help of machine learning technology and algorithms, brain tumor can be easily diagnosed by the radiologists without practicing any surgical approach. In the previous few years, remarkable progress has been observed by deep learning techniques in the domain of MIA. Although, the classification of brain tumor through Magnetic Resonance Imaging (MRI) has seen multiple problems: 1) the structure of brain and complexity of brain tissues; 2) deriving the classification of brain tumor due to brain’s nature of high-density. To study the classification of brain tumor; inculcating the normal and abnormal MRI, this study has designed a blended method by using Neutrosophic Super Resolution (NSR) with Fuzzy-C-Means (FCM) and Convolutional Neural Network (CNN).Initially, non-local mean filtered MRI provided Neutrosophic Super Resolution (NSR) image, however, for enhancement of clustering and simulation of the brain tumor along with the reduction of time consumption, efficiency and accuracy without any technical hindrance Support vector Machine (SVM) guided FCM was applied. Consequently, the recommended method resulted in an excellent performance with 98.12%, 98.2% of average success about sensitivity and 1.8% of error rate brain tumor image.


Book Synopsis Brain Tumor Classification Using Convolutional Neural Network with Neutrosophy, Super-Resolution and SVM by : Mubashir Tariq

Download or read book Brain Tumor Classification Using Convolutional Neural Network with Neutrosophy, Super-Resolution and SVM written by Mubashir Tariq and published by Infinite Study. This book was released on 2022-01-01 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the domain of Medical Image Analysis (MIA), it is difficult to perform brain tumor classification. With the help of machine learning technology and algorithms, brain tumor can be easily diagnosed by the radiologists without practicing any surgical approach. In the previous few years, remarkable progress has been observed by deep learning techniques in the domain of MIA. Although, the classification of brain tumor through Magnetic Resonance Imaging (MRI) has seen multiple problems: 1) the structure of brain and complexity of brain tissues; 2) deriving the classification of brain tumor due to brain’s nature of high-density. To study the classification of brain tumor; inculcating the normal and abnormal MRI, this study has designed a blended method by using Neutrosophic Super Resolution (NSR) with Fuzzy-C-Means (FCM) and Convolutional Neural Network (CNN).Initially, non-local mean filtered MRI provided Neutrosophic Super Resolution (NSR) image, however, for enhancement of clustering and simulation of the brain tumor along with the reduction of time consumption, efficiency and accuracy without any technical hindrance Support vector Machine (SVM) guided FCM was applied. Consequently, the recommended method resulted in an excellent performance with 98.12%, 98.2% of average success about sensitivity and 1.8% of error rate brain tumor image.

Machine Learning and Deep Learning Techniques for Medical Image Recognition

Machine Learning and Deep Learning Techniques for Medical Image Recognition

Author: Ben Othman Soufiene

Publisher: CRC Press

Published: 2023-12-01

Total Pages: 270

ISBN-13: 1003805671

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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.


Book Synopsis Machine Learning and Deep Learning Techniques for Medical Image Recognition by : Ben Othman Soufiene

Download or read book Machine Learning and Deep Learning Techniques for Medical Image Recognition written by Ben Othman Soufiene and published by CRC Press. This book was released on 2023-12-01 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Multimodal Brain Tumor Segmentation and Beyond

Multimodal Brain Tumor Segmentation and Beyond

Author: Bjoern Menze

Publisher: Frontiers Media SA

Published: 2021-08-10

Total Pages: 324

ISBN-13: 2889711706

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Book Synopsis Multimodal Brain Tumor Segmentation and Beyond by : Bjoern Menze

Download or read book Multimodal Brain Tumor Segmentation and Beyond written by Bjoern Menze and published by Frontiers Media SA. This book was released on 2021-08-10 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Imaging of Brain Tumors with Histological Correlations

Imaging of Brain Tumors with Histological Correlations

Author: Antonios Drevelegas

Publisher: Springer Science & Business Media

Published: 2013-04-17

Total Pages: 306

ISBN-13: 3662049511

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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.


Book Synopsis Imaging of Brain Tumors with Histological Correlations by : Antonios Drevelegas

Download or read book Imaging of Brain Tumors with Histological Correlations written by Antonios Drevelegas and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.