NDLI: EDNC: Ensemble Deep Neural Network for COVID-19 Recognition

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EDNC: Ensemble Deep Neural Network for COVID-19 Recognition

Content Provider	MDPI
Author	Yang, Lin Wang, Shui-Hua Zhang, Yu-Dong
Copyright Year	2022
Description	The automatic recognition of COVID-19 diseases is critical in the present pandemic since it relieves healthcare staff of the burden of screening for infection with COVID-19. Previous studies have proven that deep learning algorithms can be utilized to aid in the diagnosis of patients with potential COVID-19 infection. However, the accuracy of current COVID-19 recognition models is relatively low. Motivated by this fact, we propose three deep learning architectures, F-EDNC, FC-EDNC, and O-EDNC, to quickly and accurately detect COVID-19 infections from chest computed tomography (CT) images. Sixteen deep learning neural networks have been modified and trained to recognize COVID-19 patients using transfer learning and 2458 CT chest images. The proposed EDNC has then been developed using three of sixteen modified pre-trained models to improve the performance of COVID-19 recognition. The results suggested that the F-EDNC method significantly enhanced the recognition of COVID-19 infections with 97.75% accuracy, followed by FC-EDNC and O-EDNC (97.55% and 96.12%, respectively), which is superior to most of the current COVID-19 recognition models. Furthermore, a localhost web application has been built that enables users to easily upload their chest CT scans and obtain their COVID-19 results automatically. This accurate, fast, and automatic COVID-19 recognition system will relieve the stress of medical professionals for screening COVID-19 infections.
Ending Page	890
Page Count	22
Starting Page	869
e-ISSN	2379139X
DOI	10.3390/tomography8020071
Journal	Tomography
Issue Number	2
Volume Number	8
Language	English
Publisher	MDPI
Publisher Date	2022-03-21
Access Restriction	Open
Subject Keyword	Tomography Medical Informatics Covid-19 Ct Scans Deep Learning Transfer Learning Ensemble Automatic Recognition
Content Type	Text
Resource Type	Article

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