國立虎尾科技大學 |

Computerized systems for diagnosis and treatment of COVID-19

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computerized systems for diagnosis and treatment of COVID-19/ edited by Joao Alexandre Lobo Marques, Simon James Fong.
other author:	Marques, Joao Alexandre Lobo.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	vi, 209 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	COVID-19 (Disease) - Diagnosis -
Online resource:	https://doi.org/10.1007/978-3-031-30788-1
ISBN:	9783031307881

Computerized systems for diagnosis and treatment of COVID-19
Computerized systems for diagnosis and treatment of COVID-19[electronic resource] /edited by Joao Alexandre Lobo Marques, Simon James Fong. - Cham :Springer International Publishing :2023. - vi, 209 p. :ill., digital ;24 cm.

Clinical impact of automatic diagnostic systems -- Lung segmentation from XRay -- Lung segmentation from CT Scan -- Covid Automatic Diagnostic based on XRay -- Covid Automatic Diagnostic based on CTScan -- Cardiovascular analysis of covid patients based on ECG -- Cognitive analysis of covid patients based on EEG -- AI Controlled Mechanical Ventilator for COVID-19 patients.

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.

ISBN: 9783031307881

Standard No.: 10.1007/978-3-031-30788-1doiSubjects--Topical Terms:

1414683
COVID-19 (Disease)
--Diagnosis

LC Class. No.: RA644.C67 / C66 2023

Dewey Class. No.: 616.24144

Computerized systems for diagnosis and treatment of COVID-19
LDR:02831nam a2200325 a 4500 001 1105583
003 DE-He213
005 20230626211319.0
006 m d
007 cr nn 008maaau
008 231013s2023 sz s 0 eng d
020 $a 9783031307881 $q (electronic bk.)
020 $a 9783031307874 $q (paper)
024 7 $a 10.1007/978-3-031-30788-1 $2 doi
035 $a 978-3-031-30788-1
040 $a GP $c GP
041 0 $a eng
050 4 $a RA644.C67 $b C66 2023
072 7 $a MQW $2 bicssc
072 7 $a TEC059000 $2 bisacsh
072 7 $a MQW $2 thema
082 0 4 $a 616.24144 $2 23
090 $a RA644.C67 $b C738 2023
245 0 0 $a Computerized systems for diagnosis and treatment of COVID-19 $h [electronic resource] / $c edited by Joao Alexandre Lobo Marques, Simon James Fong.
260 $a Cham : $c 2023. $b Springer International Publishing : $b Imprint: Springer,
300 $a vi, 209 p. : $b ill., digital ; $c 24 cm.
505 0 $a Clinical impact of automatic diagnostic systems -- Lung segmentation from XRay -- Lung segmentation from CT Scan -- Covid Automatic Diagnostic based on XRay -- Covid Automatic Diagnostic based on CTScan -- Cardiovascular analysis of covid patients based on ECG -- Cognitive analysis of covid patients based on EEG -- AI Controlled Mechanical Ventilator for COVID-19 patients.
520 $a 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.
650 0 $a COVID-19 (Disease) $x Diagnosis $x Data processing. $3 1414683
650 0 $a COVID-19 (Disease) $x Treatment $x Technological innovations. $3 1414684
650 0 $a Artificial intelligence $x Medical applications. $3 600038
650 1 4 $a Biomedical Engineering and Bioengineering. $3 1211019
650 2 4 $a Therapeutics. $3 644915
650 2 4 $a Diagnosis. $3 654481
650 2 4 $a Diseases. $3 636013
650 2 4 $a Bioanalysis and Bioimaging. $3 1387818
700 1 $a Marques, Joao Alexandre Lobo. $e author. $0 (orcid)0000-0002-6472-8784 $1 https://orcid.org/0000-0002-6472-8784 $3 1361898
700 1 $a Fong, Simon. $3 787209
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-30788-1
950 $a Biomedical and Life Sciences (SpringerNature-11642)