COVID-ResNet: COVID-19 Recognition Based on Improved Attention ResNet
Abstract
:1. Introduction
2. Materials and Methods
2.1. ResNet
2.2. COVID-ResNet
2.2.1. Squeeze-and-Excitation
2.2.2. SE-Res Block (Squeeze-and-Excitation ResNet Block)
2.2.3. Coordinate Attention
2.2.4. MFCA (Multi-Layer Feature Converge Attention)
3. Results
3.1. Experimental Environment
3.2. Datasets
3.3. Experimental Results
3.4. Ablation Experiment
4. Discussion
5. Conclusions
- (1)
- COVID-ResNet achieved good results in the classification of COVID-19 CT images. In this study, the dataset of COVID-19 included COVID-19 and no COVID-19. There were other diseases in the lung, including lung cancer, tuberculosis, and so on. Applying the networks to more types and multi-source datasets is the direction for future research tasks.
- (2)
- With the development of COVID-19, COVID-19 does not only infect the lungs, but also the upper respiratory tract. It is not enough to check the lungs. Other images can be used for screening in the future.
- (3)
- In the future, other networks of deep learning can be used for auxiliary diagnosis. For example, DenseNet, Capsule Network, Googlenet, etc.
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Means | |
---|---|
features map of input | |
features map of output | |
residual function | |
identity map | |
X | features map of input about SE block |
Conversion operation | |
Features after conversion operation | |
number of channels | |
features map of output about SE block | |
feature vector | |
weight matrix | |
σ | Sigmoid |
δ | ReLU |
excitation operation | |
concat operation | |
f | the intermediate feature mapping of spatial information in coding |
F | 1 × 1 convolution |
g | weight |
Appendix B
- Confusion Matrix
Predicted as Positive Sample | Predicted as Negative Sample | Total | |
---|---|---|---|
Label as positive sample | TP (True Positive) | FN (False Negative) | TP + FN |
Label as negative sample | FP (False Positive) | TN (True Negative) | FP + TN |
Total | TP + FP | FN + TN | TP + TN + FP + FN |
- 2.
- Accuracy
- 3.
- Precision
- 4.
- Recall
- 5.
- F1 score
- 6.
- AUC (Area under Curve)
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ResNet Structural [11] | COVID-ResNet Structural | COVID-ResNet18 | Input Size | Output Size |
---|---|---|---|---|
Convolutional layer | Convolutional layer | |||
Maxpooling layer | Maxpooling layer | |||
MFCA block | ||||
Res block | SE-Res block | |||
MFCA block | ||||
Res block | SE-Res block | |||
MFCA block | ||||
Res block | SE-Res block | |||
MFCA block | ||||
Res block | SE-Res block | |||
MFCA block | ||||
Classification layer | Classification layer | Full connection |
Dataset | Train | Validation | Test | |||
---|---|---|---|---|---|---|
COVID-19 | No-COVID-19 | COVID-19 | No-COVID-19 | COVID-19 | No-COVID-19 | |
SARS-CoV-2 | 752 | 737 | 250 | 246 | 250 | 246 |
COVID-19 CT | 209 | 239 | 70 | 79 | 70 | 79 |
Model Parameter Quantity (MB) | ACC | PRE | RC | F1 | AUC | |
---|---|---|---|---|---|---|
ResNet18 [11] | 85.28 | 0.9379 | 0.9228 | 0.9569 | 0.9396 | 0.9788 |
DenseNet [15] | 53.07 | 0.9519 | 0.9537 | 0.9508 | 0.9560 | 0.9868 |
Googlenet [25] | 48.08 | 0.9473 | 0.9318 | 0.9662 | 0.9482 | 0.9871 |
ResNext50 [26] | 175.35 | 0.9457 | 0.9394 | 0.9538 | 0.9466 | 0.9863 |
SE-ResNet18 [20] | 85.96 | 0.9519 | 0.9324 | 0.9754 | 0.9534 | 0.9803 |
Xception [27] | 158.78 | 0.9426 | 0.9472 | 0.9385 | 0.9428 | 0.9799 |
Inceptionv3 [28] | 168.74 | 0.9302 | 0.9403 | 0.9200 | 0.9300 | 0.9562 |
Inceptionv4 [29] | 313.92 | 0.9581 | 0.9656 | 0.9508 | 0.9581 | 0.9811 |
EiffcienNetb0 [30] | 30.59 | 0.9395 | 0.9359 | 0.9446 | 0.9403 | 0.9802 |
COVID-ResNet | 86.94 | 0.9689 | 0.9579 | 0.9815 | 0.9696 | 0.9904 |
Parameter Quantity (MB) | ACC | PRE | RC | F1 | AUC | |
---|---|---|---|---|---|---|
Experiment 1 | 85.28 | 0.9379 | 0.9429 | 0.9662 | 0.9444 | 0.9825 |
Experiment 2 | 85.58 | 0.9535 | 0.9429 | 0.9662 | 0.9544 | 0.9867 |
Experiment 3 | 85.96 | 0.9519 | 0.9324 | 0.9754 | 0.9534 | 0.9803 |
Experiment 4 | 86.26 | 0.9550 | 0.9405 | 0.9723 | 0.9561 | 0.9857 |
Experiment 5 | 86.64 | 0.9535 | 0.9456 | 0.9631 | 0.9543 | 0.9875 |
Experiment 6 | 86.94 | 0.9628 | 0.9574 | 0.9692 | 0.9633 | 0.9935 |
Experiment 7 | 86.64 | 0.9597 | 0.9572 | 0.9631 | 0.9601 | 0.9910 |
Experiment 8 | 86.94 | 0.9689 | 0.9579 | 0.9815 | 0.9696 | 0.9904 |
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Zhou, T.; Chang, X.; Liu, Y.; Ye, X.; Lu, H.; Hu, F. COVID-ResNet: COVID-19 Recognition Based on Improved Attention ResNet. Electronics 2023, 12, 1413. https://doi.org/10.3390/electronics12061413
Zhou T, Chang X, Liu Y, Ye X, Lu H, Hu F. COVID-ResNet: COVID-19 Recognition Based on Improved Attention ResNet. Electronics. 2023; 12(6):1413. https://doi.org/10.3390/electronics12061413
Chicago/Turabian StyleZhou, Tao, Xiaoyu Chang, Yuncan Liu, Xinyu Ye, Huiling Lu, and Fuyuan Hu. 2023. "COVID-ResNet: COVID-19 Recognition Based on Improved Attention ResNet" Electronics 12, no. 6: 1413. https://doi.org/10.3390/electronics12061413
APA StyleZhou, T., Chang, X., Liu, Y., Ye, X., Lu, H., & Hu, F. (2023). COVID-ResNet: COVID-19 Recognition Based on Improved Attention ResNet. Electronics, 12(6), 1413. https://doi.org/10.3390/electronics12061413