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