Deep Learning Models for COVID-19 Detection
Abstract
:1. Introduction
- The proposed CNN learns more possible COVID-19 signs from synthetic CT images then the classic CNN models.
- The proposed novel method utilises a GAN model to generate unseen COVID-19 and normal CT images from a small database. This approach allows the CNN model to learn all possible image deformations for better modelling of the CT images. In contrast, classic CNN models build on data augmentation techniques for improved performance. However, image augmentation allows the generation of more COVID-19 and normal images with different views and orientations. This is problematic since the deformation of the lungs on CT images is the same in the generated data.
- A method is proposed for fusing synthetic and augmented CT scans for generating enhanced CNN models for COVID-19 detection.
- Data-efficient enhanced ResNet-18, ResNet-50, VGG, MobileNetV2, AlexNet, and DensNet121 models are proposed for the diagnosis of COVID-19.
2. Related Work
2.1. Convolutional Neural Networks
2.2. Generative Adversarial Networks
3. Method
3.1. COVID19-CT Database
3.2. Mosmed Database
3.3. Augmented Datasets
3.4. Synthetic CT Image Generation
3.5. Model Generations
3.6. COVID-19 Prediction
3.7. Components of the Optimisation Function
3.8. Implementation
3.9. Softwave
4. Performance Evaluation
Comparison between Classic Deep Learning Method and Proposed Data-Efficient Method
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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CT-Scan | Dataset | Train | Test |
---|---|---|---|
COVID-19 | COVID19-CT | 324 | 40 |
Normal | COVID19-CT | 293 | 37 |
COVID-19 | Mosmed | 168 | 20 |
Normal | Mosmed | 168 | 20 |
Data | CT-Scan | Dataset | Train | Test |
---|---|---|---|---|
Aug. | COVID-19 | COVID19-CT | 1393 | 40 |
Aug. | Normal | COVID19-CT | 1672 | 37 |
GAN | COVID-19 | COVID19-CT | 500 | 40 |
GAN | Normal | COVID19-CT | 500 | 37 |
Aug+GAN | Normal | COVID19-CT | 2172 | 37 |
Aug+GAN | COVID-19 | COVID19-CT | 1893 | 37 |
Aug | COVID-19 | Mosmed | 1087 | 23 |
Aug | Normal | Mosmed | 1069 | 23 |
GAN | COVID-19 | Mosmed | 128 | 23 |
GAN | Normal | Mosmed | 128 | 23 |
Aug+GAN | Normal | Mosmed | 1197 | 23 |
Aug+GAN | COVID-19 | Mosmed | 1218 | 23 |
Network | Disease | Data | AUC | ACC | SE | SP |
---|---|---|---|---|---|---|
Resnet18 | COVID19-CT | Aug | 0.77 | 0.75 | 0.83 | 0.71 |
Resnet18 | COVID19-CT | Aug+GAN | 0.89 | 0.74 | 0.88 | 0.68 |
Resnet50 | COVID19-CT | Aug | 0.71 | 0.77 | 0.86 | 0.72 |
Resnet50 | COVID19-CT | Aug+GAN | 0.81 | 0.73 | 0.95 | 0.66 |
Vgg | COVID19-CT | Aug | 0.65 | 0.75 | 0.86 | 0.70 |
Vgg | COVID19-CT | Aug+GAN | 0.67 | 0.76 | 0.87 | 0.70 |
MobileNetV2 | COVID19-CT | Aug | 0.71 | 0.73 | 0.82 | 0.69 |
MobileNetV2 | COVID19-CT | Aug+GAN | 0.77 | 0.73 | 0.84 | 0.68 |
Densenet121 | COVID19-CT | Aug | 0.70 | 0.74 | 0.87 | 0.69 |
Densenet121 | COVID19-CT | Aug+GAN | 0.77 | 0.67 | 0.92 | 0.61 |
AlexNet | COVID19-CT | Aug | 0.60 | 0.67 | 0.72 | 0.64 |
AlexNet | COVID19-CT | Aug+GAN | 0.80 | 0.69 | 0.88 | 0.64 |
AlexNet | MosMed | Aug | 0.71 | 0.70 | 1.00 | 0.63 |
AlexNet | MosMed | Aug+GAN | 0.73 | 0.66 | 0.89 | 0.60 |
MobileNetV2 | MosMed | Aug | 0.77 | 0.67 | 0.69 | 0.65 |
MobileNetV2 | MosMed | Aug+GAN | 0.84 | 0.62 | 0.65 | 0.60 |
Resnet50 | MosMed | Aug | 0.74 | 0.69 | 0.69 | 0.69 |
Resnet50 | MosMed | Aug+GAN | 0.78 | 0.69 | 0.69 | 0.69 |
Resnet18 | MosMed | Aug | 0.70 | 0.67 | 0.68 | 0.66 |
Resnet18 | MosMed | Aug+GAN | 0.75 | 0.69 | 0.69 | 0.69 |
Vgg | MosMed | MA | 0.63 | 0.69 | 0.71 | 0.68 |
Vgg | MosMed | Aug+GAN | 0.71 | 0.66 | 0.67 | 0.64 |
Densenet121 | MosMed | Aug | 0.60 | 0.65 | 0.64 | 0.65 |
Densenet121 | MosMed | Aug+GAN | 0.62 | 0.61 | 0.63 | 0.60 |
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Serte, S.; Dirik, M.A.; Al-Turjman, F. Deep Learning Models for COVID-19 Detection. Sustainability 2022, 14, 5820. https://doi.org/10.3390/su14105820
Serte S, Dirik MA, Al-Turjman F. Deep Learning Models for COVID-19 Detection. Sustainability. 2022; 14(10):5820. https://doi.org/10.3390/su14105820
Chicago/Turabian StyleSerte, Sertan, Mehmet Alp Dirik, and Fadi Al-Turjman. 2022. "Deep Learning Models for COVID-19 Detection" Sustainability 14, no. 10: 5820. https://doi.org/10.3390/su14105820