COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet

Abstract With the presence of novel coronavirus disease at the end of 2019, several approaches were proposed to help physicians detect the disease, such as using deep learning to recognize lung involvement based on the pattern of pneumonia. These approaches rely on analyzing the CT images and explor...

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Autores principales:	Malihe Javidi, Saeid Abbaasi, Sara Naybandi Atashi, Mahdi Jampour
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8f3e7cd6f773442787fa54a3fb9ca5e0
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spelling	oai:doaj.org-article:8f3e7cd6f773442787fa54a3fb9ca5e02021-12-02T17:25:33ZCOVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet10.1038/s41598-021-97901-42045-2322https://doaj.org/article/8f3e7cd6f773442787fa54a3fb9ca5e02021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97901-4https://doaj.org/toc/2045-2322Abstract With the presence of novel coronavirus disease at the end of 2019, several approaches were proposed to help physicians detect the disease, such as using deep learning to recognize lung involvement based on the pattern of pneumonia. These approaches rely on analyzing the CT images and exploring the COVID-19 pathologies in the lung. Most of the successful methods are based on the deep learning technique, which is state-of-the-art. Nevertheless, the big drawback of the deep approaches is their need for many samples, which is not always possible. This work proposes a combined deep architecture that benefits both employed architectures of DenseNet and CapsNet. To more generalize the deep model, we propose a regularization term with much fewer parameters. The network convergence significantly improved, especially when the number of training data is small. We also propose a novel Cost-sensitive loss function for imbalanced data that makes our model feasible for the condition with a limited number of positive data. Our novelties make our approach more intelligent and potent in real-world situations with imbalanced data, popular in hospitals. We analyzed our approach on two publicly available datasets, HUST and COVID-CT, with different protocols. In the first protocol of HUST, we followed the original paper setup and outperformed it. With the second protocol of HUST, we show our approach superiority concerning imbalanced data. Finally, with three different validations of the COVID-CT, we provide evaluations in the presence of a low number of data along with a comparison with state-of-the-art.Malihe JavidiSaeid AbbaasiSara Naybandi AtashiMahdi JampourNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Malihe Javidi Saeid Abbaasi Sara Naybandi Atashi Mahdi Jampour COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet
description	Abstract With the presence of novel coronavirus disease at the end of 2019, several approaches were proposed to help physicians detect the disease, such as using deep learning to recognize lung involvement based on the pattern of pneumonia. These approaches rely on analyzing the CT images and exploring the COVID-19 pathologies in the lung. Most of the successful methods are based on the deep learning technique, which is state-of-the-art. Nevertheless, the big drawback of the deep approaches is their need for many samples, which is not always possible. This work proposes a combined deep architecture that benefits both employed architectures of DenseNet and CapsNet. To more generalize the deep model, we propose a regularization term with much fewer parameters. The network convergence significantly improved, especially when the number of training data is small. We also propose a novel Cost-sensitive loss function for imbalanced data that makes our model feasible for the condition with a limited number of positive data. Our novelties make our approach more intelligent and potent in real-world situations with imbalanced data, popular in hospitals. We analyzed our approach on two publicly available datasets, HUST and COVID-CT, with different protocols. In the first protocol of HUST, we followed the original paper setup and outperformed it. With the second protocol of HUST, we show our approach superiority concerning imbalanced data. Finally, with three different validations of the COVID-CT, we provide evaluations in the presence of a low number of data along with a comparison with state-of-the-art.
format	article
author	Malihe Javidi Saeid Abbaasi Sara Naybandi Atashi Mahdi Jampour
author_facet	Malihe Javidi Saeid Abbaasi Sara Naybandi Atashi Mahdi Jampour
author_sort	Malihe Javidi
title	COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet
title_short	COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet
title_full	COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet
title_fullStr	COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet
title_full_unstemmed	COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet
title_sort	covid-19 early detection for imbalanced or low number of data using a regularized cost-sensitive capsnet
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8f3e7cd6f773442787fa54a3fb9ca5e0
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COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet

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