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COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model
World Health Organization has defined COVID-19 as a contagious, communicable and fast spreading disease engendered by the Corona virus, SARS-CoV-2, is a respirational microorganism. Computerized Tomography (CT) scan images of the chest helps in detecting COVID 19 infection in a fast way with much re...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9335012/ https://www.ncbi.nlm.nih.gov/pubmed/35919705 http://dx.doi.org/10.1016/j.advengsoft.2022.103214 |
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author | M, Madhavi P, Supraja |
author_facet | M, Madhavi P, Supraja |
author_sort | M, Madhavi |
collection | PubMed |
description | World Health Organization has defined COVID-19 as a contagious, communicable and fast spreading disease engendered by the Corona virus, SARS-CoV-2, is a respirational microorganism. Computerized Tomography (CT) scan images of the chest helps in detecting COVID 19 infection in a fast way with much reliability. In this paper, chest CT scan images of COVID and Non-COVID categories are considered to train the supervised classifier, Iterative convolution Neural Network. The training process is done with six different training data size. The trained models are iterated for the fixed size of testing data (20 images). The same set of training and testing processes are done with two different Iterative Convolutional Neural Network architectures, one with two hidden layers (CNN1) and another with three hidden layers (CNN2). The iterations are extended up to 7, but the model performance is degraded after the 6th iteration, which makes to fix the iteration level as 5 for both CNN models. Six different training sets with five iterations have led into 30 CNN models. For two different CNN architectures, which lead to 60 different models. The model designed with 100 training sets in both CNN1 and CNN2, have produced the high accuracy in COVID classification than any other models. The better classification accuracy 89% is achieved from CNN2 model with its 5th iteration. |
format | Online Article Text |
id | pubmed-9335012 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93350122022-07-29 COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model M, Madhavi P, Supraja Adv Eng Softw Research Paper World Health Organization has defined COVID-19 as a contagious, communicable and fast spreading disease engendered by the Corona virus, SARS-CoV-2, is a respirational microorganism. Computerized Tomography (CT) scan images of the chest helps in detecting COVID 19 infection in a fast way with much reliability. In this paper, chest CT scan images of COVID and Non-COVID categories are considered to train the supervised classifier, Iterative convolution Neural Network. The training process is done with six different training data size. The trained models are iterated for the fixed size of testing data (20 images). The same set of training and testing processes are done with two different Iterative Convolutional Neural Network architectures, one with two hidden layers (CNN1) and another with three hidden layers (CNN2). The iterations are extended up to 7, but the model performance is degraded after the 6th iteration, which makes to fix the iteration level as 5 for both CNN models. Six different training sets with five iterations have led into 30 CNN models. For two different CNN architectures, which lead to 60 different models. The model designed with 100 training sets in both CNN1 and CNN2, have produced the high accuracy in COVID classification than any other models. The better classification accuracy 89% is achieved from CNN2 model with its 5th iteration. Elsevier Ltd. 2022-11 2022-07-29 /pmc/articles/PMC9335012/ /pubmed/35919705 http://dx.doi.org/10.1016/j.advengsoft.2022.103214 Text en © 2022 Elsevier Ltd. All rights reserved. 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 | Research Paper M, Madhavi P, Supraja COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model |
title | COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model |
title_full | COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model |
title_fullStr | COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model |
title_full_unstemmed | COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model |
title_short | COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model |
title_sort | covid-19 infection prediction from ct scan images of lungs using iterative convolution neural network model |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9335012/ https://www.ncbi.nlm.nih.gov/pubmed/35919705 http://dx.doi.org/10.1016/j.advengsoft.2022.103214 |
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