Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans

Abstract COVID-19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emer...

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Autores principales:	Rohit Kundu, Hritam Basak, Pawan Kumar Singh, Ali Ahmadian, Massimiliano Ferrara, Ram Sarkar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	COVID-19 Deep learning Convolution neural networks Ensemble Gompertz function Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a00fb856d8a64e55afd416729f8ce804
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spelling	oai:doaj.org-article:a00fb856d8a64e55afd416729f8ce8042021-12-02T16:14:47ZFuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans10.1038/s41598-021-93658-y2045-2322https://doaj.org/article/a00fb856d8a64e55afd416729f8ce8042021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93658-yhttps://doaj.org/toc/2045-2322Abstract COVID-19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.Rohit KunduHritam BasakPawan Kumar SinghAli AhmadianMassimiliano FerraraRam SarkarNature PortfolioarticleCOVID-19Deep learningConvolution neural networksEnsembleGompertz functionMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 Deep learning Convolution neural networks Ensemble Gompertz function Medicine R Science Q
spellingShingle	COVID-19 Deep learning Convolution neural networks Ensemble Gompertz function Medicine R Science Q Rohit Kundu Hritam Basak Pawan Kumar Singh Ali Ahmadian Massimiliano Ferrara Ram Sarkar Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans
description	Abstract COVID-19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.
format	article
author	Rohit Kundu Hritam Basak Pawan Kumar Singh Ali Ahmadian Massimiliano Ferrara Ram Sarkar
author_facet	Rohit Kundu Hritam Basak Pawan Kumar Singh Ali Ahmadian Massimiliano Ferrara Ram Sarkar
author_sort	Rohit Kundu
title	Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans
title_short	Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans
title_full	Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans
title_fullStr	Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans
title_full_unstemmed	Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans
title_sort	fuzzy rank-based fusion of cnn models using gompertz function for screening covid-19 ct-scans
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a00fb856d8a64e55afd416729f8ce804
work_keys_str_mv	AT rohitkundu fuzzyrankbasedfusionofcnnmodelsusinggompertzfunctionforscreeningcovid19ctscans AT hritambasak fuzzyrankbasedfusionofcnnmodelsusinggompertzfunctionforscreeningcovid19ctscans AT pawankumarsingh fuzzyrankbasedfusionofcnnmodelsusinggompertzfunctionforscreeningcovid19ctscans AT aliahmadian fuzzyrankbasedfusionofcnnmodelsusinggompertzfunctionforscreeningcovid19ctscans AT massimilianoferrara fuzzyrankbasedfusionofcnnmodelsusinggompertzfunctionforscreeningcovid19ctscans AT ramsarkar fuzzyrankbasedfusionofcnnmodelsusinggompertzfunctionforscreeningcovid19ctscans
_version_	1718384307824754688

Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans

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