EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images

Abstract The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To mitigate the impact of the coronavirus a reliable, fast, and accurate diagnostic system should be promptly implemented. In this study, we pro...

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Autores principales:	Seyed Ziae Mousavi Mojab, Seyedmohammad Shams, Farshad Fotouhi, Hamid Soltanian-Zadeh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/04cc48e64d5b4808832af9d829d17ca5
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spelling	oai:doaj.org-article:04cc48e64d5b4808832af9d829d17ca52021-11-08T10:52:39ZEpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images10.1038/s41598-021-00524-y2045-2322https://doaj.org/article/04cc48e64d5b4808832af9d829d17ca52021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00524-yhttps://doaj.org/toc/2045-2322Abstract The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To mitigate the impact of the coronavirus a reliable, fast, and accurate diagnostic system should be promptly implemented. In this study, we propose EpistoNet, a decision tree-based ensemble model using two mixtures of discriminative experts to classify COVID-19 lung infection from chest X-ray images. To optimize the architecture and hyper-parameters of the designed neural networks, we employed Epistocracy algorithm, a recently proposed hyper-heuristic evolutionary method. Using 2500 chest X-ray images consisting of 1250 COVID-19 and 1250 non-COVID-19 cases, we left out 500 images for testing and partitioned the remaining 2000 images into 5 different clusters using K-means clustering algorithm. We trained multiple deep convolutional neural networks on each cluster to help build a mixture of strong discriminative experts from the top-performing models supervised by a gating network. The final ensemble model obtained 95% accuracy on COVID-19 images and 93% accuracy on non-COVID-19. The experimental results show that EpistoNet can accurately, and reliably be used to detect COVID-19 infection in the chest X-ray images, and Epistocracy algorithm can be effectively used to optimize the hyper-parameters of the proposed models.Seyed Ziae Mousavi MojabSeyedmohammad ShamsFarshad FotouhiHamid Soltanian-ZadehNature 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 Seyed Ziae Mousavi Mojab Seyedmohammad Shams Farshad Fotouhi Hamid Soltanian-Zadeh EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images
description	Abstract The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To mitigate the impact of the coronavirus a reliable, fast, and accurate diagnostic system should be promptly implemented. In this study, we propose EpistoNet, a decision tree-based ensemble model using two mixtures of discriminative experts to classify COVID-19 lung infection from chest X-ray images. To optimize the architecture and hyper-parameters of the designed neural networks, we employed Epistocracy algorithm, a recently proposed hyper-heuristic evolutionary method. Using 2500 chest X-ray images consisting of 1250 COVID-19 and 1250 non-COVID-19 cases, we left out 500 images for testing and partitioned the remaining 2000 images into 5 different clusters using K-means clustering algorithm. We trained multiple deep convolutional neural networks on each cluster to help build a mixture of strong discriminative experts from the top-performing models supervised by a gating network. The final ensemble model obtained 95% accuracy on COVID-19 images and 93% accuracy on non-COVID-19. The experimental results show that EpistoNet can accurately, and reliably be used to detect COVID-19 infection in the chest X-ray images, and Epistocracy algorithm can be effectively used to optimize the hyper-parameters of the proposed models.
format	article
author	Seyed Ziae Mousavi Mojab Seyedmohammad Shams Farshad Fotouhi Hamid Soltanian-Zadeh
author_facet	Seyed Ziae Mousavi Mojab Seyedmohammad Shams Farshad Fotouhi Hamid Soltanian-Zadeh
author_sort	Seyed Ziae Mousavi Mojab
title	EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images
title_short	EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images
title_full	EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images
title_fullStr	EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images
title_full_unstemmed	EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images
title_sort	epistonet: an ensemble of epistocracy-optimized mixture of experts for detecting covid-19 on chest x-ray images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/04cc48e64d5b4808832af9d829d17ca5
work_keys_str_mv	AT seyedziaemousavimojab epistonetanensembleofepistocracyoptimizedmixtureofexpertsfordetectingcovid19onchestxrayimages AT seyedmohammadshams epistonetanensembleofepistocracyoptimizedmixtureofexpertsfordetectingcovid19onchestxrayimages AT farshadfotouhi epistonetanensembleofepistocracyoptimizedmixtureofexpertsfordetectingcovid19onchestxrayimages AT hamidsoltanianzadeh epistonetanensembleofepistocracyoptimizedmixtureofexpertsfordetectingcovid19onchestxrayimages
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EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images

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