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Deep Learning Applications in Image Analysis
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.
R 10,257.00
Deep Learning in Medical Image Processing and Analysis
Medical images, in various formats, are used by clinicians to identify abnormalities or markers associated with certain conditions, such as cancers, diseases, abnormalities or other adverse health conditions. Deep learning algorithms use vast volumes of data to train the computer to recognise certain features in the images that are associated with the disease or condition that you wish to identify. Whilst analysing the images by eye can take a lot of time, deep learning algorithms have the benefit of reviewing medical images at a faster rate than a human can, which aids the clinician, speeding up diagnoses and freeing up clinicians' time for other duties. Deep Learning in Medical Image Processing and Analysis introduces the fundamentals of deep learning for biomedical image analysis for applications including ophthalmology, cancer detection and heart disease. The book considers the principles of multi-instance feature selection, swarm optimisation, parallel processing models, artificial neural networks, support vector machines, as well as their design and optimisation, in biomedical applications. Topics such as data security, patient confidentiality, effectiveness and reliability will also be discussed. Written by an international team of experts, this edited book covers principles and applications for industry and academic researchers, scientists, engineers, developers, and designers in the fields of machine learning, deep learning, AI, image processing, signal processing, computer science or related fields. It will also be of interest to standards bodies and regulators, and clinicians using deep learning models.
R 4,417.00
Deep Learning for Medical Image Analysis
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.
R 4,763.00
Deep Learning for Medical Image Analysis
Deep learning is providing exciting solutions for medical image analysis problems and is seen as 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 have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.
R 3,678.00
Deep Learning in Medical Image Analysis
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.
R 9,041.96
Deep Learning Applications
A comprehensive compilation that explores a wide range of applications where deep learning techniques have had impact and its practical uses. The book covers fields like healthcare, finance, autonomous systems, and more. The methodologies such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and reinforcement learning, have been included.In healthcare deep learning is used for medical image analysis, disease prediction, and personalized medicine. In the financial sector, aspects like fraud detection, algorithmic trading, and risk management are reviewed. Also autonomous systems, including self-driving cars and drones are explored.Each chapter includes detailed case studies, experimental results, and insights from leading experts, offering readers both foundational knowledge and advanced techniques. The book serves as a valuable resource for researchers, practitioners, and students seeking to understand the breadth and depth of deep learning applications.
R 6,118.00
Deep Learning in Medical Image Analysis
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.
R 8,971.71
Deep Learning Applications in Medical Image Segmentation
Apply revolutionary deep learning technology to the fast-growing field of medical image segmentation Precise medical image segmentation is rapidly becoming one of the most important tools in medical research, diagnosis, and treatment. The potential for deep learning, a technology which is already revolutionizing practice across hundreds of subfields, is immense. The prospect of using deep learning to address the traditional shortcomings of image segmentation demands close inspection and wide proliferation of relevant knowledge. Deep Learning Applications in Medical Image Segmentation meets this demand with a comprehensive introduction and its growing applications. Covering foundational concepts and its advanced techniques, it offers a one-stop resource for researchers and other readers looking for a detailed understanding of the topic. It is deeply engaged with the main challenges and recent advances in the field of deep-learning-based medical image segmentation. Readers will also find: Analysis of deep learning models, including FCN, UNet, SegNet, Dee Lab, and many moreDetailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systemsRecent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structuresAnalyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosisExplores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentationIdentifies and discusses the key challenges faced in medical image segmentation using deep learning techniquesProvides an overview of the latest advancements, applications, and future trends in deep learning for medical image analysis Deep Learning Applications in Medical Image Segmentation is ideal for academics and researchers working with medical image segmentation, as well as professionals in medical imaging, data science, and biomedical engineering.
R 5,048.00
Advances in Deep Learning for Medical Image Analysis
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.
R 2,779.83
MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities
Deep learning has become a trending area of research due to its adaptive characteristics and high levels of applicability. In recent years, researchers have begun applying deep learning strategies to image analysis and pattern recognition for solving technical issues within image classification. As these technologies continue to advance, professionals have begun translating this intelligent programming language into mobile applications for devices. Programmers and web developers are in need of significant research on how to successfully develop pattern recognition applications using intelligent programming. MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities is an essential reference source that presents a solution to developing intelligent pattern recognition Apps on iOS devices based on MatConvNet deep learning. Featuring research on topics such as medical image diagnosis, convolutional neural networks, and character classification, this book is ideally designed for programmers, developers, researchers, practitioners, engineers, academicians, students, scientists, and educators seeking coverage on the specific development of iOS mobile applications using pattern recognition strategies.
R 7,529.42
Advances in Deep Learning for Medical Image Analysis
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.
R 7,559.85
Deep Learning Methods for Medical Image Analysis
This volume is a useful resource on the utilization of deep learning methodologies in medical imaging, a domain that is swiftly transforming clinical diagnosis and treatment of diseases. It presents fundamental concepts in deep learning and advances to sophisticated techniques, including Convolutional Neural Networks (CNNs) for feature extraction, Fully Convolutional Networks (FCNs) and U-Nets for accurate image segmentation, and Generative Adversarial Networks (GANs) for data augmentation and image enhancement. It examines other Long Short-Term Memory (LSTM) networks for temporal analysis for elucidating intricate spatial connections in high-resolution medical pictures. It comprises a mix of theoretical background, examples of how these strategies have been put into practice, and case studies of both the triumphs and failures of these approaches in actual medical settings. This book highlights the extensive use of deep learning in several medical fields by discussing its potential applications in areas such as early illness identification, retinal imaging, histopathological categorization, and tumor and lesion detection in MRIs and CT scans. The book delves deeper than only technical topics to tackle the specific issues that arise with medical data. These include the importance of data scarcity in instances of uncommon diseases, the ethical concerns related to data privacy and security, and the necessity of interpretability to build confidence among clinicians. To help researchers overcome typical challenges, we also investigate strategies like synthetic data generation, multimodal learning, and transfer learning.
R 5,129.00
R 2,796.00
Deep Learning for Multimedia Processing Applications
Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing.Divided into two volumes, Volume Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos.Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts.Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.
R 2,711.00
Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments
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.
R 12,541.37
Big Data Analysis and Deep Learning Applications
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.
R 7,326.00
Deep Learning for Multimedia Processing Applications
Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing.Divided into two volumes, Volume One begins by introducing the fundamental concepts of deep learning, providing readers with a solid foundation to understand its relevance in multimedia processing. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos.Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts.Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.
R 2,711.00
Deep Learning Applications in Medical Imaging
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.
R 11,308.17
Deep Learning for Multimedia Processing Applications
Deep Learning for Multimedia Processing is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing.Divided into two volumes, Volume One begins by introducing the fundamental concepts of deep learning, providing readers with a solid foundation to understand its relevance in multimedia processing.Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos.Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts.Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.
R 6,118.00
Deep Learning for Multimedia Processing Applications
Deep Learning for Multimedia Processing is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing.Divided into two volumes, Volumes Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos.Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts.Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.
R 6,118.00
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
R 2,191.23
Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
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.
R 5,690.63
Deep Learning: Algorithms and Applications
This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
R 7,326.00
Machine Learning and Deep Learning Techniques for Medical Image Recognition
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 ML and DL approaches toward developing prediction tools and models and improving medical diagnosis Teaches how ML and DL algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy, and pathology and so forth Covers common research problems in medical image analysis and their challenges Focusses 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.
R 7,576.15
Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
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.
R 6,232.26
Deep Learning for Hyperspectral Image Analysis and Classification
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 theoriginal contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
R 5,376.13
Approaches and Applications of Deep Learning in Virtual Medical Care
The recent advancements in the machine learning paradigm have various applications, however, it has shown significant results in the field of medical data analysis. The results are highly accurate and are comparable to human experts. The various research has proved the high accuracy of deep learning algorithms and has become a standard choice for analysing medical data, especially medical images, video, and electronic health records. Deep learning methods applied to electronic health records are contributing to understanding the evolution of chronic diseases and predicting the risk of developing those diseases. Researchers in industry, hospitals, and academia have published hundreds of scientific contributions in this area during a pandemic.This book is an ideal and relevant source of content for data science and healthcare professionals who want to delve into complex deep learning algorithms, calibrate models, and improve the predictions of the trained model on medical imaging.Primary audiences for this book are professionals and researchers in the fields of data science, machine learning, deep learning, and AI. Also academicians, healthcare professionals, or anyone who may have a keen interest in how the machine and deep learning algorithms are helping in the identification of solutions to medical sensor/image data analysis, event detection, segmentation, and abnormality detection, object/lesion classification, organ/region/landmark localization, object/lesion detection, organ/substructure segmentation, lesion segmentation, and medical image registration. The variety of readers in the fields of government, consulting, healthcare professionals, as well as the readers from all the social strata, can also be benefited from this book to improve understanding of the cutting-edge theory, technologies, methodologies, and applications of deep Learning algorithms for medical care.
R 13,458.32
Deep Learning for Medical Applications with Unique Data
Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems.
R 5,002.00
Understanding and Interpreting Machine Learning in Medical Image Computing Applications
This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.
R 2,152.44
Examining the Impact of Deep Learning and IoT on Multi-Industry Applications
Deep learning, as a recent AI technique, has proven itself efficient in solving many real-world problems. Deep learning algorithms are efficient, high performing, and an effective standard for solving these problems. In addition, with IoT, deep learning is in many emerging and developing domains of computer technology. Deep learning algorithms have brought a revolution in computer vision applications by introducing an efficient solution to several image processing-related problems that have long remained unresolved or moderately solved. Various significant IoT technologies in various industries, such as education, health, transportation, and security, combine IoT with deep learning for complex problem solving and the supported interaction between human beings and their surroundings.Examining the Impact of Deep Learning and IoT on Multi-Industry Applications provides insights on how deep learning, together with IoT, impacts various sectors such as healthcare, agriculture, cyber security, and social media analysis applications. The chapters present solutions to various real-world problems using these methods from various researchers' points of view. While highlighting topics such as medical diagnosis, power consumption, livestock management, security, and social media analysis, this book is ideal for IT specialists, technologists, security analysts, medical practitioners, imaging specialists, diagnosticians, academicians, researchers, industrial experts, scientists, and undergraduate and postgraduate students who are working in the field of computer engineering, electronics, and electrical engineering.
R 7,926.65
Deep Learning: Algorithms and Applications
This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
R 8,511.14
Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods
Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book 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. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases.
R 4,414.00
Deep Learning for Hyperspectral Image Analysis and Classification
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.
R 5,626.91
Handbook of Research on Deep Learning Innovations and Trends
Leading technology firms and research institutions are continuously exploring new techniques in artificial intelligence and machine learning. As such, deep learning has now been recognized in various real-world applications such as computer vision, image processing, biometrics, pattern recognition, and medical imaging. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient.The Handbook of Research on Deep Learning Innovations and Trends is an essential scholarly resource that presents current trends and the latest research on deep learning and explores the concepts, algorithms, and techniques of data mining and analysis. Highlighting topics such as computer vision, encryption systems, and biometrics, this book is ideal for researchers, practitioners, industry professionals, students, and academicians.
R 11,389.07
Deep Learning for Data Analytics
Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.
R 5,002.00
Deep Learning in Healthcare
This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems.Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data.Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.
R 5,138.65
Computer Analysis of Images and Patterns
This volume LNCS 14184 and 14185 constitutes the refereed proceedings of the 20th International Conference, CAIP 2023, in Limassol, Cyprus, in September 2023. The 54 full papers presented were carefully reviewed and selected from 67 submissions. They were organized in the following section as follows:Part I: PAR Contest 2023; Deep Learning; Machine Learning for Image and Pattern Analysis; and Object Recognition and Segmentation.Part II : Biometrics- Human Pose Estimation- Action Recognition; Biomedical Image and Pattern Analysis; and General Vision- AI Applications.
R 2,746.78
Trends in Deep Learning Methodologies
Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.
R 4,414.00
Computer Analysis of Images and Patterns
This volume LNCS 14184 and 14185 constitutes the refereed proceedings of the 20th International Conference, CAIP 2023, in Limassol, Cyprus, in September 2023. The 54 full papers presented were carefully reviewed and selected from 67 submissions. They were organized in the following section as follows:Part I-:PAR Contest 2023; Deep Learning; Machine Learning for Image and Pattern Analysis; and Object Recognition and Segmentation.Part II : Biometrics- Human Pose Estimation- Action Recognition; Biomedical Image and Pattern Analysis; and General Vision- AI Applications.
R 2,751.79
Hyperspectral Image Analysis
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
R 5,956.00
State of the Art in Neural Networks and Their Applications
State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing and suitable data analytics useful for clinical diagnosis and research applications are covered, including relevant case studies. The application of Neural Network, Artificial Intelligence, and Machine Learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more.
R 5,002.00
R 1,512.00
Image Analysis
This two-volume set (LNCS 13885-13886) constitutes the refereed proceedings of the 23rd Scandinavian Conference on Image Analysis, SCIA 2023, held in Lapland, Finland, in April 2023.The 67 revised papers presented were carefully reviewed and selected from 108 submissions. The contributions are structured in topical sections on datasets and evaluation; action and behaviour recognition; image and video processing, analysis, and understanding; detection, recognition, classification, and localization in 2D and/or 3D; machine learning and deep learning; segmentation, grouping, and shape; vision for robotics and autonomous vehicles; biometrics, faces, body gestures and pose; 3D vision from multiview and other sensors; vision applications and systems.
R 3,536.69
Deep Learning and Data Labeling for Medical Applications
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.
R 2,173.43
Hyperspectral Image Analysis
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
R 5,416.65
Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications
Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India.Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.
R 2,957.42
