Shulph Ink
Computational Modelling and Imaging for SARS-CoV-2 and COVID-19
Computational Modelling and Imaging for SARS-CoV-2 and COVID-19
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- More about Computational Modelling and Imaging for SARS-CoV-2 and COVID-19
This book aims to present new computational techniques and methodologies for analyzing the clinical, epidemiological, and public health aspects of the SARS-CoV-2 and COVID-19 pandemics, including the use of soft computing techniques such as machine learning algorithms for epidemiological analysis, computational image processing algorithms for COVID-19 lesion detection, and computational methods for analyzing SARS-CoV-2 infection. It also discusses conventional epidemiological models and machine learning techniques for predicting the course of the epidemic and provides real-life examples through case studies. The book is intended for biomedical engineers, mathematicians, postgraduate students, researchers, and medical scientists working on identifying and tracking infectious diseases.
Format: Paperback / softback
Length: 146 pages
Publication date: 25 September 2023
Publisher: Taylor & Francis Ltd
The aim of this comprehensive book is to present cutting-edge computational techniques and methodologies for analyzing the clinical, epidemiological, and public health aspects of the SARS-CoV-2 and COVID-19 pandemic. It delves into the utilization of soft computing techniques, including machine learning algorithms, to examine the epidemiological dimensions of the SARS-CoV-2 virus. The book offers a clear and concise explanation of novel computational image processing algorithms for the accurate detection of COVID-19 lesions in lung CT and X-ray images. It explores a range of computational methods for computerized analysis of SARS-CoV-2 infection, including severity assessment. Furthermore, the book provides a detailed description of the algorithms that have the potential to facilitate large-scale screening of SARS-CoV-2 infected cases. Additionally, it elucidates conventional epidemiological models and machine learning techniques for predicting the course of the COVID-19 epidemic. Real-life examples are provided through comprehensive case studies, making the content accessible and relevant to a wide audience, including biomedical engineers, mathematicians, postgraduate students, researchers, and medical scientists engaged in identifying and tracking infectious diseases.
Weight: 453g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367696245
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