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A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia
A large number of reports present artificial intelligence (AI) algorithms, which support pneumonia detection caused by COVID-19 from chest CT (computed tomography) scans. Only a few studies provided access to the source code, which limits the analysis of the out-of-distribution generalization abilit...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319098/ https://www.ncbi.nlm.nih.gov/pubmed/35885433 http://dx.doi.org/10.3390/diagnostics12071527 |
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author | Furtado, Adhvan da Purificação, Carlos Alberto Campos Badaró, Roberto Nascimento, Erick Giovani Sperandio |
author_facet | Furtado, Adhvan da Purificação, Carlos Alberto Campos Badaró, Roberto Nascimento, Erick Giovani Sperandio |
author_sort | Furtado, Adhvan |
collection | PubMed |
description | A large number of reports present artificial intelligence (AI) algorithms, which support pneumonia detection caused by COVID-19 from chest CT (computed tomography) scans. Only a few studies provided access to the source code, which limits the analysis of the out-of-distribution generalization ability. This study presents Cimatec-CovNet-19, a new light 3D convolutional neural network inspired by the VGG16 architecture that supports COVID-19 identification from chest CT scans. We trained the algorithm with a dataset of 3000 CT Scans (1500 COVID-19-positive) with images from different parts of the world, enhanced with 3000 images obtained with data augmentation techniques. We introduced a novel pre-processing approach to perform a slice-wise selection based solely on the lung CT masks and an empirically chosen threshold for the very first slice. It required only 16 slices from a CT examination to identify COVID-19. The model achieved a recall of 0.88, specificity of 0.88, ROC-AUC of 0.95, PR-AUC of 0.95, and F1-score of 0.88 on a test set with 414 samples (207 COVID-19). These results support Cimatec-CovNet-19 as a good and light screening tool for COVID-19 patients. The whole code is freely available for the scientific community. |
format | Online Article Text |
id | pubmed-9319098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93190982022-07-27 A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia Furtado, Adhvan da Purificação, Carlos Alberto Campos Badaró, Roberto Nascimento, Erick Giovani Sperandio Diagnostics (Basel) Article A large number of reports present artificial intelligence (AI) algorithms, which support pneumonia detection caused by COVID-19 from chest CT (computed tomography) scans. Only a few studies provided access to the source code, which limits the analysis of the out-of-distribution generalization ability. This study presents Cimatec-CovNet-19, a new light 3D convolutional neural network inspired by the VGG16 architecture that supports COVID-19 identification from chest CT scans. We trained the algorithm with a dataset of 3000 CT Scans (1500 COVID-19-positive) with images from different parts of the world, enhanced with 3000 images obtained with data augmentation techniques. We introduced a novel pre-processing approach to perform a slice-wise selection based solely on the lung CT masks and an empirically chosen threshold for the very first slice. It required only 16 slices from a CT examination to identify COVID-19. The model achieved a recall of 0.88, specificity of 0.88, ROC-AUC of 0.95, PR-AUC of 0.95, and F1-score of 0.88 on a test set with 414 samples (207 COVID-19). These results support Cimatec-CovNet-19 as a good and light screening tool for COVID-19 patients. The whole code is freely available for the scientific community. MDPI 2022-06-23 /pmc/articles/PMC9319098/ /pubmed/35885433 http://dx.doi.org/10.3390/diagnostics12071527 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Furtado, Adhvan da Purificação, Carlos Alberto Campos Badaró, Roberto Nascimento, Erick Giovani Sperandio A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia |
title | A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia |
title_full | A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia |
title_fullStr | A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia |
title_full_unstemmed | A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia |
title_short | A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia |
title_sort | light deep learning algorithm for ct diagnosis of covid-19 pneumonia |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319098/ https://www.ncbi.nlm.nih.gov/pubmed/35885433 http://dx.doi.org/10.3390/diagnostics12071527 |
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