Name
Title | ||
|---|---|---|
Efficient Deep Neural Networks For Classification Of Covid-19 Based On Ct Images: Virtualization Via Software Defined Radio |
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%. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.comcom.2021.06.011 | COMPUTER COMMUNICATIONS |
Keywords | DocType | Volume |
Computed tomography, ResNet-50, VGG-16, Convolutional neural networks (CNN), Convolutional auto-encoder neural network, (CAENN), COVID-19 | Journal | 176 |
ISSN | Citations | PageRank |
0140-3664 | 3 | 0.38 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saman Fouladi | 1 | 3 | 0.38 |
| M. J. Ebadi | 2 | 8 | 1.45 |
| Ali A Safaei | 3 | 3 | 0.38 |
| Mohd Yazid Bajuri | 4 | 3 | 0.38 |
| Ali Ahmadian | 5 | 67 | 13.67 |