Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem 1st Edition Alex 1003837336 9781003837336

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Product details:

• ISBN-10: 1003837336
• ISBN-13: 9781003837336
• Author: Alex Khang

This book examines computer vision and IoT-integrated technologies used by medical professionals in decision-making, for sustainable development in a healthcare ecosystem, and to better serve patients and stakeholders. It looks at the methodologies, technologies, models, frameworks, and practices necessary to resolve the challenging issues associated with leveraging the emerging technologies driving the medical field. The chapters discuss machine vision, AI-driven computer vision, machine learning, deep learning, AI-integrated IoT technology, data science, blockchain, AR/VR technology, cloud data, and cybersecurity techniques in designing and implementing a smart healthcare infrastructure in the era of the Industrial Revolution 4.0. Techniques are applied to the detection, diagnosis, and monitoring of a wide range of health issues. Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem targets a mixed audience of students, engineers, researchers, academics, and professionals who are researching and working in the field of medical and healthcare industries from different environments and countries.

Table of contents:

Chapter 1 Application of Computer Vision (CV) in the Healthcare Ecosystem
Chapter 2 Artificial Intelligence (AI)-Assisted Computer Vision (CV) in Healthcare Systems
Chapter 3 Computer Vision (CV)-based Machine Learning (ML) Models for the Healthcare System
Chapter 4 Computer Vision (CV)-Aided Medical Diagnosis for Cardiovascular Disease Detection
Chapter 5 Artificial Intelligence (AI)-Aided Diagnosis System to Objectively Measure Chronic Pain
Chapter 6 Artificial Intelligence (AI)-Enabled Technology in Medicine-Advancing Holistic Healthcare Monitoring and Control Systems
Chapter 7 Medical and Biomedical Signal Processing and Prediction Using the EEG Machine and Electroencephalography
Chapter 8 Artificial Intelligence (AI)-Aided Computer Vision (CV) in Healthcare System
Chapter 9 Artificial Intelligence (AI) Models for Disease Diagnosis and Prediction of Heart Disease with Artificial Neural Networks (ANN)
Chapter 10 Harnessing Deep Learning (DL) for Image Inpainting in Healthcare System-Methods and Challenges
Chapter 11 Skin Cancer Classification Using ConvNeXtLarge Architecture
Chapter 12 Brain Tumor Detection Using TensorFlow Framework
Chapter 13 Early Prediction of Sepsis with the Predictive Analysis Model Using 1.5 Million Records
Chapter 14 An Efficient FPGA Implementation of Approximate Multiply Accumulate Unit for Image and Video Processing Applications in Healthcare Sector
Chapter 15 Lung Cancer Prediction Using Convolutional Neural Network (CNN) with VGG16 Model
Chapter 16 Identifying Error and Bias in Chest Radiographic Images for COVID Detection Using Deep Learning Algorithms
Chapter 17 Forecast of Health Risk for Chronic Kidney Disease: A Comparison between Naïve Bayes (NB) and Support Vector Machine (SVM) Models
Chapter 18 The Performance of Feature Selection Approaches on Boosted Random Forest Algorithms for Predicting Cardiovascular Disease
Chapter 19 Application of Artificial Intelligence (AI) Technologies in Employing Chatbots to Access Mental Health
Chapter 20 Clinical Decision Support Systems in Smart Medical Ecosystem
Chapter 21 The Future of Edge Computing for Healthcare Ecosystem
Chapter 22 Privacy-Aware IoT-Based Multi-Disease Diagnosis Model for Healthcare System
Chapter 23 Using Big Data to Solve Problems in the Field of Medicine
Chapter 24 Automations and Robotics Improves Quality Healthcare in t

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