Bøger af Ayman El-Baz

Today's healthcare organizations must focus on a lot more than just the health of their clients. The infrastructure it takes to support clinical-care delivery continues to expand, with information technology being one of the most significant contributors to that growth. As companies have become more dependent on technology for their clinical, administrative, and financial functions, their IT departments and expenditures have had to scale quickly to keep up. However, as technology demands have increased, so have the options for reliable infrastructure for IT applications and data storage. The one that has taken center stage over the past few years is cloud computing. Healthcare researchers are moving their efforts to the cloud because they need adequate resources to process, store, exchange, and use large quantities of medical data.Cloud Computing in Medical Imaging covers the state-of-the-art techniques for cloud computing in medical imaging, healthcare technologies, and services. The book focuses onMachine-learning algorithms for health data securityFog computing in IoT-based health careMedical imaging and healthcare applications using fog IoT networksDiagnostic imaging and associated servicesImage steganography for medical informaticsThis book aims to help advance scientific research within the broad field of cloud computing in medical imaging, healthcare technologies, and services. It focuses on major trends and challenges in this area and presents work aimed to identify new techniques and their use in biomedical analysis.

This book focuses on major trends and challenges in the area of diagnosing lung and kidney cancer using artificial intelligence tools. It presents work aimed to identify new techniques and their use in biomedical analysis.

This book focuses on major trends and challenges in the area of attention deficit hyperactivity disorder (ADHD) and presents work aimed to identify new techniques and their use in biomedical analysis.

Ultra-brief CopyThis book focuses on major trends and challenges in the detection of lung cancer, presenting work aimed at identifying new techniques and their use in biomedical analysis. Volume 1 examines the main applications of computer-aided diagnosis to lung cancer. It is ideal for academics, industry professionals and advanced students.

The utilization of AI strategies for diagnosis, follow-up, and treatments of COVID-19 patients is now becoming essential. This is the first comprehensive reference work published detailing the latest research and developments in the utilization of AI strategies in the diagnosis and treatment of COVID-19 patients.

This book provides a brief introduction to medical imaging, stochastic modeling, and model-guided image analysis. It presents the latest state of the art in stochastic modeling for medical image analysis while incorporating fully tested experimental results throughout. This valuable resource details efficient stochastic modeling techniques, incl