A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19

Abstract Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. In this paper, a lightweight convolutional neural network (CNN) model named multi-scale gated multi-head attention depthwise separable CNN (MGMADS-CNN) is proposed, which is based on...

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Autores principales:	Geng Hong, Xiaoyan Chen, Jianyong Chen, Miao Zhang, Yumeng Ren, Xinyu Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a0ee4193f36c4891b41283052878d3c6
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spelling	oai:doaj.org-article:a0ee4193f36c4891b41283052878d3c62021-12-02T18:03:05ZA multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-1910.1038/s41598-021-97428-82045-2322https://doaj.org/article/a0ee4193f36c4891b41283052878d3c62021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97428-8https://doaj.org/toc/2045-2322Abstract Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. In this paper, a lightweight convolutional neural network (CNN) model named multi-scale gated multi-head attention depthwise separable CNN (MGMADS-CNN) is proposed, which is based on attention mechanism and depthwise separable convolution. A multi-scale gated multi-head attention mechanism is designed to extract effective feature information from the COVID-19 X-ray and CT images for classification. Moreover, the depthwise separable convolution layers are adopted as MGMADS-CNN’s backbone to reduce the model size and parameters. The LeNet-5, AlexNet, GoogLeNet, ResNet, VGGNet-16, and three MGMADS-CNN models are trained, validated and tested with tenfold cross-validation on X-ray and CT images. The results show that MGMADS-CNN with three attention layers (MGMADS-3) has achieved accuracy of 96.75% on X-ray images and 98.25% on CT images. The specificity and sensitivity are 98.06% and 96.6% on X-ray images, and 98.17% and 98.05% on CT images. The size of MGMADS-3 model is only 43.6 M bytes. In addition, the detection speed of MGMADS-3 on X-ray images and CT images are 6.09 ms and 4.23 ms for per image, respectively. It is proved that the MGMADS-3 can detect and classify COVID-19 faster with higher accuracy and efficiency.Geng HongXiaoyan ChenJianyong ChenMiao ZhangYumeng RenXinyu ZhangNature 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 Geng Hong Xiaoyan Chen Jianyong Chen Miao Zhang Yumeng Ren Xinyu Zhang A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19
description	Abstract Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. In this paper, a lightweight convolutional neural network (CNN) model named multi-scale gated multi-head attention depthwise separable CNN (MGMADS-CNN) is proposed, which is based on attention mechanism and depthwise separable convolution. A multi-scale gated multi-head attention mechanism is designed to extract effective feature information from the COVID-19 X-ray and CT images for classification. Moreover, the depthwise separable convolution layers are adopted as MGMADS-CNN’s backbone to reduce the model size and parameters. The LeNet-5, AlexNet, GoogLeNet, ResNet, VGGNet-16, and three MGMADS-CNN models are trained, validated and tested with tenfold cross-validation on X-ray and CT images. The results show that MGMADS-CNN with three attention layers (MGMADS-3) has achieved accuracy of 96.75% on X-ray images and 98.25% on CT images. The specificity and sensitivity are 98.06% and 96.6% on X-ray images, and 98.17% and 98.05% on CT images. The size of MGMADS-3 model is only 43.6 M bytes. In addition, the detection speed of MGMADS-3 on X-ray images and CT images are 6.09 ms and 4.23 ms for per image, respectively. It is proved that the MGMADS-3 can detect and classify COVID-19 faster with higher accuracy and efficiency.
format	article
author	Geng Hong Xiaoyan Chen Jianyong Chen Miao Zhang Yumeng Ren Xinyu Zhang
author_facet	Geng Hong Xiaoyan Chen Jianyong Chen Miao Zhang Yumeng Ren Xinyu Zhang
author_sort	Geng Hong
title	A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19
title_short	A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19
title_full	A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19
title_fullStr	A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19
title_full_unstemmed	A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19
title_sort	multi-scale gated multi-head attention depthwise separable cnn model for recognizing covid-19
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a0ee4193f36c4891b41283052878d3c6
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A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19

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