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Computational Modelling and Imaging for Sars-Cov-2 and Covid-19

by S. Prabha

Computational methods for COVID-19 analysis

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AlbakiReads Editorial

Computational methods for COVID-19 analysis

This edited volume brings together computational approaches to several aspects of SARS-CoV-2 and COVID-19: epidemiological analysis, medical-image processing, infection severity assessment, and epidemic-course prediction. Its coverage includes machine-learning methods alongside conventional epidemiological models, with particular attention to detecting COVID-19 lesions in lung CT and X-ray images. Algorithm descriptions and case studies connect the methods to possible screening and tracking applications. The book sits at the intersection of biomedical imaging, computation, and infectious-disease research, presenting tools for analyzing both individual clinical images and broader patterns of an epidemic.

Good fit for readers who enjoy

  • Biomedical engineers and medical scientists working on infectious-disease detection or tracking
  • Researchers and mathematicians interested in computational epidemiology or medical imaging
  • Postgraduate students studying machine learning applications in health

Themes

Computational epidemiologyMachine learningMedical-image analysisCOVID-19 severity assessmentInfectious-disease screening

Based on publisher information and book metadata.

Book overview

The aim of this book is to present new computational techniques and methodologies for the analysis of the clinical, epidemiological and public health aspects of SARS-CoV-2 and COVID-19 pandemic. The book presents the use of soft computing techniques such as machine learning algorithms for analysis of the epidemiological aspects of the SARS-CoV-2. This book clearly explains novel computational image processing algorithms for the detection of COVID-19 lesions in lung CT and X-ray images. It explores various computational methods for computerized analysis of the SARS-CoV-2 infection including severity assessment. The book provides a detailed description of the algorithms which can potentially aid in mass screening of SARS-CoV-2 infected cases. Finally the book also explains the conventional epidemiological models and machine learning techniques for the prediction of the course of the COVID-19 epidemic. It also provides real life examples through case studies. The book is intended for biomedical engineers, mathematicians, postgraduate students; researchers; medical scientists working on identifying and tracking infectious diseases.

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