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A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans

The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as...

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Autores principales: Hasija, Sanskar, Akash, Peddaputha, Bhargav Hemanth, Maganti, Kumar, Ankit, Sharma, Sanjeev
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier Masson SAS. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8958781/
https://www.ncbi.nlm.nih.gov/pubmed/36741276
http://dx.doi.org/10.1016/j.neuri.2022.100069
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author Hasija, Sanskar
Akash, Peddaputha
Bhargav Hemanth, Maganti
Kumar, Ankit
Sharma, Sanjeev
author_facet Hasija, Sanskar
Akash, Peddaputha
Bhargav Hemanth, Maganti
Kumar, Ankit
Sharma, Sanjeev
author_sort Hasija, Sanskar
collection PubMed
description The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of the virus as soon as possible could contain and avoid a serious COVID-19 outbreak. Current pharmaceutical techniques and diagnostic methods tests such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Serology tests are time-consuming, expensive, and require a well-equipped laboratory for analysis, making them restrictive and inaccessible to everyone. Deep Learning has grown in popularity in recent years, and it now plays a crucial role in Image Classification, which also involves Medical Imaging. Using chest CT scans, this study explores the problem statement automation of differentiating COVID-19 contaminated individuals from healthy individuals. Convolutional Neural Networks (CNNs) can be trained to detect patterns in computed tomography scans (CT scans). Hence, different CNN models were used in the current study to identify variations in chest CT scans, with accuracies ranging from 91% to 98%. The Multiclass Classification method is used to build these architectures. This study also proposes a new approach for classifying CT images that use two binary classifications combined to work together, achieving 98.38% accuracy. All of these architectures' performances are compared using different classification metrics.
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spelling pubmed-89587812022-03-28 A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans Hasija, Sanskar Akash, Peddaputha Bhargav Hemanth, Maganti Kumar, Ankit Sharma, Sanjeev Neurosci Inform Artificial Intelligence in Brain Informatics The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of the virus as soon as possible could contain and avoid a serious COVID-19 outbreak. Current pharmaceutical techniques and diagnostic methods tests such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Serology tests are time-consuming, expensive, and require a well-equipped laboratory for analysis, making them restrictive and inaccessible to everyone. Deep Learning has grown in popularity in recent years, and it now plays a crucial role in Image Classification, which also involves Medical Imaging. Using chest CT scans, this study explores the problem statement automation of differentiating COVID-19 contaminated individuals from healthy individuals. Convolutional Neural Networks (CNNs) can be trained to detect patterns in computed tomography scans (CT scans). Hence, different CNN models were used in the current study to identify variations in chest CT scans, with accuracies ranging from 91% to 98%. The Multiclass Classification method is used to build these architectures. This study also proposes a new approach for classifying CT images that use two binary classifications combined to work together, achieving 98.38% accuracy. All of these architectures' performances are compared using different classification metrics. The Author(s). Published by Elsevier Masson SAS. 2022-12 2022-03-28 /pmc/articles/PMC8958781/ /pubmed/36741276 http://dx.doi.org/10.1016/j.neuri.2022.100069 Text en © 2022 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Artificial Intelligence in Brain Informatics
Hasija, Sanskar
Akash, Peddaputha
Bhargav Hemanth, Maganti
Kumar, Ankit
Sharma, Sanjeev
A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans
title A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans
title_full A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans
title_fullStr A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans
title_full_unstemmed A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans
title_short A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans
title_sort novel approach for detection of covid-19 and pneumonia using only binary classification from chest ct-scans
topic Artificial Intelligence in Brain Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8958781/
https://www.ncbi.nlm.nih.gov/pubmed/36741276
http://dx.doi.org/10.1016/j.neuri.2022.100069
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