Deep learning applications image (45 Ergebnisse)

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Taschenbuch. Zustand: Neu. Introduction to Deep Learning Business Applications for Developers | From Conversational Bots in Customer Service to Medical Image Processing | Armando Vieira (u. a.) | Taschenbuch | xxi | Englisch | 2018 | Apress | EAN 9781484234525 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Taschenbuch. Zustand: Neu. Metaheuristic - Based Deep Learning for Medical Image Segmentation | Theory and Applications | Mohammed Khouy (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208441791 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

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Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.…

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Zustand: New. KlappentextDeep 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, .

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Kartoniert / Broschiert. Zustand: New. Investigates novel concepts of deep learning for acquisition of non-invasive biomedical image and signal modalities for different disorders Explores the implementation of novel deep learning and CNN methodologies and their impact studies t.

Buch. Zustand: Neu. Neuware - Technical depth and ethical frameworks for AI visual media synthesis Generative AI models for visual media are transforming virtual reality and biomedical imaging while raising urgent questions about deepfakes and misinformation. AI-Generated Image and Video Synthesis addresses both dimensions. A team of researchers provide algorithmic foundations alongside detection strategies, authentication methods, and regulatory analysis. Coverage spans text-to-image generation, image-to-image translation, video synthesis, neural rendering, and 3D-aware generation. The book examines AI applications in CT, MRI synthetic data augmentation, and virtual staining for biomedical contexts. Case studies explore AI-assisted filmmaking, music videos, and style transfer. A dedicated chapter forecasts emerging trends including diffusion-transformer hybrids and autonomous generative agents. Readers will also find: - Comparative analyses of generative models including GANs, diffusion models, and transformers with implementation guidance and code repositories for hands-on experimentation - Deepfake detection strategies and digital content authentication techniques addressing misinformation, intellectual property rights, and emerging regulatory frameworks worldwide - Industry case studies demonstrating real-world deployments in creative industries, surveillance systems, education, and cultural preservation applications - Biomedical imaging applications covering synthetic data generation for CT and MRI, virtual staining techniques, and data augmentation strategies - Practical toolkits supporting implementation and evaluation of AI synthesis techniques across professional and academic contexts Designed for AI researchers, computer vision engineers, and graduate students studying deep learning and image processing, this book connects theoretical principles with practical deployment. The combination of technical depth, application coverage, and ethical analysis makes it a comprehensive resource for professionals navigating AI-generated visual media.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a valuable resource for understanding the transformative role of artificial intelligence in modern healthcare and aims to inspire continued research and collaboration across disciplines. In recent years, deep learning has emerged as a transformative technology across various fields, with medical image analysis standing out as one of its most impactful applications. This book offers a comprehensive overview of the latest developments in this fast-evolving domain, bridging foundational principles with state-of-the-art techniques that are redefining the future of medical imaging.This book is structured in two parts Part I: Deep Learning Fundamentals and Paradigms and Part II: Advanced Deep Learning for Medical Image Analysis. The book provides in-depth coverage of essential topics, including convolutional neural networks, attention mechanisms, transformer architectures, multimodal analysis, semi-supervised learning, domain adaptation, generative models, and foundation models for large-scale pretraining.This book is intended for a broad audience, including graduate students, academic researchers, and industry professionals in computer science, biomedical engineering, and healthcare technologies. It serves as both an introductory guide and a reference resource for those seeking to deepen their knowledge in this rapidly evolving area.…

Buch. Zustand: Neu. Neuware - 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 more- Detailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systems- Recent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structures- Analyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosis- Explores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentation- Identifies and discusses the key challenges faced in medical image segmentation using deep learning techniques- Provides 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.…

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