Marques J.A.L., Fong S.J. (ed.) Computerized Systems for Diagnosis and Treatment of COVID-19

Cham: Springer, 2023. — 210 p.This book describes the application of signal and image processing technologies, artificial intelligence, and machine learning techniques to support Covid-19 diagnosis and treatment. The book focuses on two main applications: critical diagnosis requiring high precision and speed, and treatment of symptoms, including those affecting the cardiovascular and neurological systems.
The areas discussed in this book range from signal processing, time series analysis, and image segmentation to detection and classification. Technical approaches include deep learning, transfer learning, transformers, AutoML, and other machine learning techniques that can be considered not only for Covid-19 issues but also for different medical applications, with slight adjustments to the problem under study.
The Covid-19 pandemic has impacted the entire world and changed how societies and individuals interact. Due to the high infection and mortality rates, and the multiple consequences of the virus infection in the human body, the challenges were vast and enormous. These necessitated the integration of different disciplines to address the problems. As a global response, researchers across academia and industry made several developments to provide computational solutions to support epidemiologic, managerial, and health/medical decisions. To that end, this book provides state-of-the-art information on the most advanced solutions.Technology Developments to Face the COVID-19 Pandemic: Advances, Challenges, and Trends
Lung Segmentation of Chest X-Rays Using Unet Convolutional Networks
Segmentation of CT-Scan Images Using UNet Network for Patients Diagnosed with COVID-19
Covid-19 Detection Based on Chest X-Ray Images Using Multiple Transfer Learning CNN Models
X-Ray Machine Learning Classification with VGG-16 for Feature Extraction
Classification of COVID-19 CT Scans Using Convolutional Neural Networks and Transformers
COVID-19 Classification Using CT Scans with Convolutional Neural Networks
TPOT Automated Machine Learning Approach for Multiple Diagnostic Classification of Lung Radiography and Feature Extraction
Evaluation of ECG Non-linear Features in Time-Frequency Domain for the Discrimination of COVID-19 Severity Stages
Classification of Severity of COVID-19 Patients Based on the Heart Rate Variability
Exploratory Data Analysis on Clinical and Emotional Parameters of Pregnant Women with COVID-19 Symptoms