Detection and analysis of COVID-19 in medical images using deep learning techniques

Abstract The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the C...

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Autores principales:	Dandi Yang, Cristhian Martinez, Lara Visuña, Hardev Khandhar, Chintan Bhatt, Jesus Carretero
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0e11f898e1e54fb7930e0a8c38edb33f
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spelling	oai:doaj.org-article:0e11f898e1e54fb7930e0a8c38edb33f2021-12-02T18:01:48ZDetection and analysis of COVID-19 in medical images using deep learning techniques10.1038/s41598-021-99015-32045-2322https://doaj.org/article/0e11f898e1e54fb7930e0a8c38edb33f2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99015-3https://doaj.org/toc/2045-2322Abstract The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out the best architecture, pre-processing, and training parameters for the models largely automatically. The accuracy and F1-score were both above 96% in the diagnosis of COVID-19 using CT-scan images. In addition, we applied transfer learning techniques to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced VGG16 deep transfer learning architecture. High accuracy of 99% was achieved by enhanced VGG16 in the detection of X-ray images from COVID-19 and pneumonia. The accuracy and validity of the algorithms were assessed on X-ray and CT-scan well-known public datasets. The proposed methods have better results for COVID-19 diagnosis than other related in literature. In our opinion, our work can help virologists and radiologists to make a better and faster diagnosis in the struggle against the outbreak of COVID-19.Dandi YangCristhian MartinezLara VisuñaHardev KhandharChintan BhattJesus CarreteroNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dandi Yang Cristhian Martinez Lara Visuña Hardev Khandhar Chintan Bhatt Jesus Carretero Detection and analysis of COVID-19 in medical images using deep learning techniques
description	Abstract The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out the best architecture, pre-processing, and training parameters for the models largely automatically. The accuracy and F1-score were both above 96% in the diagnosis of COVID-19 using CT-scan images. In addition, we applied transfer learning techniques to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced VGG16 deep transfer learning architecture. High accuracy of 99% was achieved by enhanced VGG16 in the detection of X-ray images from COVID-19 and pneumonia. The accuracy and validity of the algorithms were assessed on X-ray and CT-scan well-known public datasets. The proposed methods have better results for COVID-19 diagnosis than other related in literature. In our opinion, our work can help virologists and radiologists to make a better and faster diagnosis in the struggle against the outbreak of COVID-19.
format	article
author	Dandi Yang Cristhian Martinez Lara Visuña Hardev Khandhar Chintan Bhatt Jesus Carretero
author_facet	Dandi Yang Cristhian Martinez Lara Visuña Hardev Khandhar Chintan Bhatt Jesus Carretero
author_sort	Dandi Yang
title	Detection and analysis of COVID-19 in medical images using deep learning techniques
title_short	Detection and analysis of COVID-19 in medical images using deep learning techniques
title_full	Detection and analysis of COVID-19 in medical images using deep learning techniques
title_fullStr	Detection and analysis of COVID-19 in medical images using deep learning techniques
title_full_unstemmed	Detection and analysis of COVID-19 in medical images using deep learning techniques
title_sort	detection and analysis of covid-19 in medical images using deep learning techniques
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0e11f898e1e54fb7930e0a8c38edb33f
work_keys_str_mv	AT dandiyang detectionandanalysisofcovid19inmedicalimagesusingdeeplearningtechniques AT cristhianmartinez detectionandanalysisofcovid19inmedicalimagesusingdeeplearningtechniques AT laravisuna detectionandanalysisofcovid19inmedicalimagesusingdeeplearningtechniques AT hardevkhandhar detectionandanalysisofcovid19inmedicalimagesusingdeeplearningtechniques AT chintanbhatt detectionandanalysisofcovid19inmedicalimagesusingdeeplearningtechniques AT jesuscarretero detectionandanalysisofcovid19inmedicalimagesusingdeeplearningtechniques
_version_	1718378910614290432

Detection and analysis of COVID-19 in medical images using deep learning techniques

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