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Impact of Early Termination of Lockdown and Maintaining Social Distancing: COVID-19 Read chapter Read first chapter

2022 | OriginalPaper | Chapter

6. Computational Intelligence Against Covid-19 for Diagnosis Using Image Processing Techniques in Healthcare Sector

Authors : Manikandan Ramachandran, Rajkumar Kulandaivel, Hariharan Kalyanaraman, Murugan Subramanian, Ambeshwar Kumar

Published in: How COVID-19 is Accelerating the Digital Revolution

Publisher: Springer International Publishing

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Abstract

Coronavirus 2019 (COVID-19) medical images detection and classification are used in artificial intelligence (AI) techniques. Few months back, from the observation it is witnessed that there is a rapid increase in using AI techniques for diagnosing COVID-19 with chest computed tomography (CT) images. AI more accurately detects COVID-19; moreover efficiently differentiates this from other lung infection and pneumonia. AI is very useful and has been broadly accepted in medical applications as its accuracy and prediction rates are high. This paper is developed and aims to fight against corona through AI using computational intelligence in detecting and classifying COVID-19 using Densnet-121 architecture on chest CT images from a global diverse multi-institution dataset. Furthermore, data from clinics and images from medical applications improve the performance of the proposed approach and provide better response with practical applications. Classification performance was evaluated by confusion matrices followed by overall accuracy, precision, recall and specificity for precisely classifying COVID-19 against any condition.

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Metadata
Title
Computational Intelligence Against Covid-19 for Diagnosis Using Image Processing Techniques in Healthcare Sector
Authors
Manikandan Ramachandran
Rajkumar Kulandaivel
Hariharan Kalyanaraman
Murugan Subramanian
Ambeshwar Kumar
Copyright Year
2022
Book
How COVID-19 is Accelerating the Digital Revolution

Print ISBN: 978-3-030-98166-2

Electronic ISBN: 978-3-030-98167-9

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-98167-9_6

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