Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio.
Comput Commun
; 176: 234-248, 2021 Aug 01.
Article
in English
| MEDLINE | ID: covidwho-1272369
ABSTRACT
The novel 2019 coronavirus disease (COVID-19) has infected over 141 million people worldwide since April 20, 2021. More than 200 countries around the world have been affected by the coronavirus pandemic. Screening for COVID-19, we use fast and inexpensive images from computed tomography (CT) scans. In this paper, ResNet-50, VGG-16, convolutional neural network (CNN), convolutional auto-encoder neural network (CAENN), and machine learning (ML) methods are proposed for classifying Chest CT Images of COVID-19. The dataset consists of 1252 CT scans that are positive and 1230 CT scans that are negative for COVID-19 virus. The proposed models have priority over the other models that there is no need of pre-trained networks and data augmentation for them. The classification accuracies of ResNet-50, VGG-16, CNN, and CAENN were obtained 92.24%, 94.07%, 93.84%, and 93.04% respectively. Among ML classifiers, the nearest neighbor (NN) had the highest performance with an accuracy of 94%.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Language:
English
Journal:
Comput Commun
Year:
2021
Document Type:
Article
Affiliation country:
J.comcom.2021.06.011
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