Deep Learning Applications In Image Analysis

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Deep Learning in Medical Image Analysis

Author : Gobert Lee,Hiroshi Fujita
Publisher : Springer Nature
Page : 184 pages
File Size : 43,9 Mb
Release : 2020-02-06
Category : Medical
ISBN : 9783030331283

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Deep Learning in Medical Image Analysis by Gobert Lee,Hiroshi Fujita Pdf

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

Biomedical Data Mining for Information Retrieval

Author : Sujata Dash,Subhendu Kumar Pani,S. Balamurugan,Ajith Abraham
Publisher : John Wiley & Sons
Page : 450 pages
File Size : 47,5 Mb
Release : 2021-08-24
Category : Computers
ISBN : 9781119711247

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Biomedical Data Mining for Information Retrieval by Sujata Dash,Subhendu Kumar Pani,S. Balamurugan,Ajith Abraham Pdf

BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

Deep Learning Applications in Image Analysis

Author : Sanjiban Sekhar Roy,Ching-Hsien Hsu,Venkateshwara Kagita
Publisher : Springer Nature
Page : 218 pages
File Size : 48,5 Mb
Release : 2023-07-08
Category : Technology & Engineering
ISBN : 9789819937844

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Deep Learning Applications in Image Analysis by Sanjiban Sekhar Roy,Ching-Hsien Hsu,Venkateshwara Kagita Pdf

This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3. The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.

Machine Learning in Image Analysis and Pattern Recognition

Author : Munish Kumar , R. K. Sharma,Ishwar Sethi
Publisher : MDPI
Page : 112 pages
File Size : 45,5 Mb
Release : 2021-09-08
Category : Technology & Engineering
ISBN : 9783036517148

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Machine Learning in Image Analysis and Pattern Recognition by Munish Kumar , R. K. Sharma,Ishwar Sethi Pdf

This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.

Deep Learning for Image Processing Applications

Author : D.J. Hemanth,V. Vieira Estrela
Publisher : IOS Press
Page : 284 pages
File Size : 41,8 Mb
Release : 2017-12
Category : Computers
ISBN : 9781614998228

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Deep Learning for Image Processing Applications by D.J. Hemanth,V. Vieira Estrela Pdf

Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.

Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments

Author : Raj, Alex Noel Joseph,Mahesh, Vijayalakshmi G. V.,Nersisson, Ruban
Publisher : IGI Global
Page : 381 pages
File Size : 53,8 Mb
Release : 2020-12-25
Category : Computers
ISBN : 9781799866923

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Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments by Raj, Alex Noel Joseph,Mahesh, Vijayalakshmi G. V.,Nersisson, Ruban Pdf

Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.

Deep Learning for Medical Image Analysis

Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publisher : Academic Press
Page : 544 pages
File Size : 40,6 Mb
Release : 2023-12-01
Category : Computers
ISBN : 9780323858885

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Deep Learning for Medical Image Analysis by S. Kevin Zhou,Hayit Greenspan,Dinggang Shen Pdf

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is 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 are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest 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 cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

Deep Learning in Object Detection and Recognition

Author : Xiaoyue Jiang,Abdenour Hadid,Yanwei Pang,Eric Granger,Xiaoyi Feng
Publisher : Springer
Page : 0 pages
File Size : 51,7 Mb
Release : 2020-11-27
Category : Computers
ISBN : 9811506515

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Deep Learning in Object Detection and Recognition by Xiaoyue Jiang,Abdenour Hadid,Yanwei Pang,Eric Granger,Xiaoyi Feng Pdf

This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.

Medical Image Analysis

Author : Alejandro Frangi,Jerry Prince,Milan Sonka
Publisher : Academic Press
Page : 700 pages
File Size : 44,9 Mb
Release : 2023-09-20
Category : Technology & Engineering
ISBN : 9780128136584

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Medical Image Analysis by Alejandro Frangi,Jerry Prince,Milan Sonka Pdf

Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. Provides an authoritative description of key concepts and methods Includes tutorial-based sections that clearly explain principles and their application to different medical domains Presents a representative selection of topics to match a modern and relevant approach to medical image computing

Handbook of Deep Learning Applications

Author : Valentina Emilia Balas,Sanjiban Sekhar Roy,Dharmendra Sharma,Pijush Samui
Publisher : Springer
Page : 383 pages
File Size : 41,7 Mb
Release : 2019-02-25
Category : Technology & Engineering
ISBN : 9783030114794

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Handbook of Deep Learning Applications by Valentina Emilia Balas,Sanjiban Sekhar Roy,Dharmendra Sharma,Pijush Samui Pdf

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

Deep Learning in Computer Vision

Author : Mahmoud Hassaballah,Ali Ismail Awad
Publisher : CRC Press
Page : 261 pages
File Size : 51,8 Mb
Release : 2020-03-23
Category : Computers
ISBN : 9781351003803

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Deep Learning in Computer Vision by Mahmoud Hassaballah,Ali Ismail Awad Pdf

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

Advances in Deep Learning for Medical Image Analysis

Author : Archana Mire,Vinayak Elangovan,Shailaja Patil
Publisher : CRC Press
Page : 168 pages
File Size : 44,8 Mb
Release : 2022-04-28
Category : Technology & Engineering
ISBN : 9781000575958

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Advances in Deep Learning for Medical Image Analysis by Archana Mire,Vinayak Elangovan,Shailaja Patil Pdf

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

Deep Learning Applications in Medical Imaging

Author : Saxena, Sanjay,Paul, Sudip
Publisher : IGI Global
Page : 274 pages
File Size : 50,6 Mb
Release : 2020-10-16
Category : Medical
ISBN : 9781799850724

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Deep Learning Applications in Medical Imaging by Saxena, Sanjay,Paul, Sudip Pdf

Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.

Deep Learning for Hyperspectral Image Analysis and Classification

Author : Linmi Tao,Atif Mughees
Publisher : Springer Nature
Page : 207 pages
File Size : 51,8 Mb
Release : 2021-02-20
Category : Computers
ISBN : 9789813344204

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Deep Learning for Hyperspectral Image Analysis and Classification by Linmi Tao,Atif Mughees Pdf

This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

Big Data Analysis and Deep Learning Applications

Author : Thi Thi Zin,Jerry Chun-Wei Lin
Publisher : Springer
Page : 386 pages
File Size : 54,9 Mb
Release : 2018-06-06
Category : Technology & Engineering
ISBN : 9789811308697

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Big Data Analysis and Deep Learning Applications by Thi Thi Zin,Jerry Chun-Wei Lin Pdf

This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities.