Deep learning representations to support COVID-19 diagnosis on CT slices
Introduction: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease f...
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ftdoajarticles:oai:doaj.org/article:21b384a2a1f141549dc6e006e65e12a2 2023-05-15T15:10:33+02:00 Deep learning representations to support COVID-19 diagnosis on CT slices Josué Ruano John Arcila David Romo-Bucheli Carlos Vargas Jefferson Rodríguez Óscar Mendoza Miguel Plazas Lola Bautista Jorge Villamizar Gabriel Pedraza Alejandra Moreno Diana Valenzuela Lina Vázquez Carolina Valenzuela-Santos Paul Camacho Daniel Mantilla Fabio Martínez Carrillo 2022-03-01T00:00:00Z https://doi.org/10.7705/biomedica.5927 https://doaj.org/article/21b384a2a1f141549dc6e006e65e12a2 EN ES eng spa Instituto Nacional de Salud https://revistabiomedica.org/index.php/biomedica/article/view/5927 https://doaj.org/toc/0120-4157 0120-4157 doi:10.7705/biomedica.5927 https://doaj.org/article/21b384a2a1f141549dc6e006e65e12a2 Biomédica: revista del Instituto Nacional de Salud, Vol 42, Iss 1, Pp 170-183 (2022) coronavirus infections/diagnosis tomography x-ray computed deep learning Medicine R Arctic medicine. Tropical medicine RC955-962 article 2022 ftdoajarticles https://doi.org/10.7705/biomedica.5927 2022-12-31T02:08:52Z Introduction: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist’s expertise, which may result in subjective evaluations. Objective: To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples. Materials and methods: Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic’s dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers. Results: The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively. Conclusion: Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Biomédica 42 1 170 183 |
institution |
Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English Spanish |
topic |
coronavirus infections/diagnosis tomography x-ray computed deep learning Medicine R Arctic medicine. Tropical medicine RC955-962 |
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coronavirus infections/diagnosis tomography x-ray computed deep learning Medicine R Arctic medicine. Tropical medicine RC955-962 Josué Ruano John Arcila David Romo-Bucheli Carlos Vargas Jefferson Rodríguez Óscar Mendoza Miguel Plazas Lola Bautista Jorge Villamizar Gabriel Pedraza Alejandra Moreno Diana Valenzuela Lina Vázquez Carolina Valenzuela-Santos Paul Camacho Daniel Mantilla Fabio Martínez Carrillo Deep learning representations to support COVID-19 diagnosis on CT slices |
topic_facet |
coronavirus infections/diagnosis tomography x-ray computed deep learning Medicine R Arctic medicine. Tropical medicine RC955-962 |
description |
Introduction: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist’s expertise, which may result in subjective evaluations. Objective: To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples. Materials and methods: Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic’s dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers. Results: The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively. Conclusion: Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings. |
format |
Article in Journal/Newspaper |
author |
Josué Ruano John Arcila David Romo-Bucheli Carlos Vargas Jefferson Rodríguez Óscar Mendoza Miguel Plazas Lola Bautista Jorge Villamizar Gabriel Pedraza Alejandra Moreno Diana Valenzuela Lina Vázquez Carolina Valenzuela-Santos Paul Camacho Daniel Mantilla Fabio Martínez Carrillo |
author_facet |
Josué Ruano John Arcila David Romo-Bucheli Carlos Vargas Jefferson Rodríguez Óscar Mendoza Miguel Plazas Lola Bautista Jorge Villamizar Gabriel Pedraza Alejandra Moreno Diana Valenzuela Lina Vázquez Carolina Valenzuela-Santos Paul Camacho Daniel Mantilla Fabio Martínez Carrillo |
author_sort |
Josué Ruano |
title |
Deep learning representations to support COVID-19 diagnosis on CT slices |
title_short |
Deep learning representations to support COVID-19 diagnosis on CT slices |
title_full |
Deep learning representations to support COVID-19 diagnosis on CT slices |
title_fullStr |
Deep learning representations to support COVID-19 diagnosis on CT slices |
title_full_unstemmed |
Deep learning representations to support COVID-19 diagnosis on CT slices |
title_sort |
deep learning representations to support covid-19 diagnosis on ct slices |
publisher |
Instituto Nacional de Salud |
publishDate |
2022 |
url |
https://doi.org/10.7705/biomedica.5927 https://doaj.org/article/21b384a2a1f141549dc6e006e65e12a2 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Biomédica: revista del Instituto Nacional de Salud, Vol 42, Iss 1, Pp 170-183 (2022) |
op_relation |
https://revistabiomedica.org/index.php/biomedica/article/view/5927 https://doaj.org/toc/0120-4157 0120-4157 doi:10.7705/biomedica.5927 https://doaj.org/article/21b384a2a1f141549dc6e006e65e12a2 |
op_doi |
https://doi.org/10.7705/biomedica.5927 |
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Biomédica |
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42 |
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1 |
container_start_page |
170 |
op_container_end_page |
183 |
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1766341556987494400 |