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TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model
BACKGROUND: As of Feb 27, 2022, coronavirus (COVID-19) has caused 434,888,591 infections and 5,958,849 deaths worldwide, dealing a severe blow to the economies and cultures of most countries around the world. As the virus has mutated, its infectious capacity has further increased. Effective diagnosi...
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/PMC9013277/ https://www.ncbi.nlm.nih.gov/pubmed/35489140 http://dx.doi.org/10.1016/j.compbiomed.2022.105531 |
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author | Sun, Junding Pi, Pengpeng Tang, Chaosheng Wang, Shui-Hua Zhang, Yu-Dong |
author_facet | Sun, Junding Pi, Pengpeng Tang, Chaosheng Wang, Shui-Hua Zhang, Yu-Dong |
author_sort | Sun, Junding |
collection | PubMed |
description | BACKGROUND: As of Feb 27, 2022, coronavirus (COVID-19) has caused 434,888,591 infections and 5,958,849 deaths worldwide, dealing a severe blow to the economies and cultures of most countries around the world. As the virus has mutated, its infectious capacity has further increased. Effective diagnosis of suspected cases is an important tool to stop the spread of the pandemic. Therefore, we intended to develop a computer-aided diagnosis system for the diagnosis of suspected cases. METHODS: To address the shortcomings of commonly used pre-training methods and exploit the information in unlabeled images, we proposed a new pre-training method based on transfer learning with self-supervised learning (TS). After that, a new convolutional neural network based on attention mechanism and deep residual network (RANet) was proposed to extract features. Based on this, a hybrid ensemble model (TSRNet) was proposed for classifying lung CT images of suspected patients as COVID-19 and normal. RESULTS: Compared with the existing five models in terms of accuracy (DarkCOVIDNet: 98.08%; Deep-COVID: 97.58%; NAGNN: 97.86%; COVID-ResNet: 97.78%; Patch-based CNN: 88.90%), TSRNet has the highest accuracy of 99.80%. In addition, the recall, f1-score, and AUC of the model reached 99.59%, 99.78%, and 1, respectively. CONCLUSION: TSRNet can effectively diagnose suspected COVID-19 cases with the help of the information in unlabeled and labeled images, thus helping physicians to adopt early treatment plans for confirmed cases. |
format | Online Article Text |
id | pubmed-9013277 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90132772022-04-18 TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model Sun, Junding Pi, Pengpeng Tang, Chaosheng Wang, Shui-Hua Zhang, Yu-Dong Comput Biol Med Article BACKGROUND: As of Feb 27, 2022, coronavirus (COVID-19) has caused 434,888,591 infections and 5,958,849 deaths worldwide, dealing a severe blow to the economies and cultures of most countries around the world. As the virus has mutated, its infectious capacity has further increased. Effective diagnosis of suspected cases is an important tool to stop the spread of the pandemic. Therefore, we intended to develop a computer-aided diagnosis system for the diagnosis of suspected cases. METHODS: To address the shortcomings of commonly used pre-training methods and exploit the information in unlabeled images, we proposed a new pre-training method based on transfer learning with self-supervised learning (TS). After that, a new convolutional neural network based on attention mechanism and deep residual network (RANet) was proposed to extract features. Based on this, a hybrid ensemble model (TSRNet) was proposed for classifying lung CT images of suspected patients as COVID-19 and normal. RESULTS: Compared with the existing five models in terms of accuracy (DarkCOVIDNet: 98.08%; Deep-COVID: 97.58%; NAGNN: 97.86%; COVID-ResNet: 97.78%; Patch-based CNN: 88.90%), TSRNet has the highest accuracy of 99.80%. In addition, the recall, f1-score, and AUC of the model reached 99.59%, 99.78%, and 1, respectively. CONCLUSION: TSRNet can effectively diagnose suspected COVID-19 cases with the help of the information in unlabeled and labeled images, thus helping physicians to adopt early treatment plans for confirmed cases. Elsevier Ltd. 2022-07 2022-04-16 /pmc/articles/PMC9013277/ /pubmed/35489140 http://dx.doi.org/10.1016/j.compbiomed.2022.105531 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 | Article Sun, Junding Pi, Pengpeng Tang, Chaosheng Wang, Shui-Hua Zhang, Yu-Dong TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model |
title | TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model |
title_full | TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model |
title_fullStr | TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model |
title_full_unstemmed | TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model |
title_short | TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model |
title_sort | tsrnet: diagnosis of covid-19 based on self-supervised learning and hybrid ensemble model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013277/ https://www.ncbi.nlm.nih.gov/pubmed/35489140 http://dx.doi.org/10.1016/j.compbiomed.2022.105531 |
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