COVID-19 radiograph prognosis using a deep CResNeXt network.

Autor: Yadav DP; Department of Computer Engineering & Applications, G.L.A. University, Mathura, UP India., Jalal AS; Department of Computer Engineering & Applications, G.L.A. University, Mathura, UP India., Goyal A; Department of Electrical Engineering and Computer Science, Texas A&M University, Kingsville, TX USA., Mishra A; Department of Electrical Engineering and Computer Science, Texas A&M University, Kingsville, TX USA., Uprety K; The University of Tennessee Health Science Center, Memphis, TN USA., Guragai N; Department of Cardiology, St. Joseph Regional Medical Center, Paterson, NJ USA.
Jazyk: angličtina
Zdroj: Multimedia tools and applications [Multimed Tools Appl] 2023 Mar 08, pp. 1-27. Date of Electronic Publication: 2023 Mar 08.
DOI: 10.1007/s11042-023-14960-7
Abstrakt: COVID-19 has caused an epidemic in the entire world and it is caused by the novel virus SARS-COV-2. In severe conditions, this virus can cause a critical lung infection or viral pneumonia. To administer the correct treatment to patients, COVID-19 testing is important for diagnosing and determining patients who are infected with COVID-19, as opposed to those infected with other bacterial or viral infections. In this paper, a CResNeXt chest radiograph COVID-19 prediction model is proposed using residual network architecture. The advantage of the proposed model is that it requires lesser free hyper-parameters as compared to other residual networks. In addition, the training time per epochs of the model is very less compared to VGG19, ResNet-50, ResNeXt. The proposed CResNeXt model's binary classification (COVID-19 versus No-Finding) accuracy is observed to be 98.63% and 99.99% and multi-class classification (COVID-19, Pneumonia, and No-Finding) accuracy is observed to be 97.42% and 99.27% on the original and augmented datasets, respectively.
Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest.
(© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)
Databáze: MEDLINE
Externí odkaz: