Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME
Abstract
:1. Introduction
- A detailed analysis of the results obtained and comparison with the performance of the same models being applied to independent datasets of either CT scans or X-ray images;
- Finally, we explain the models’ predictability considering top features with Local Interpretable Model-Agnostic Explanations (LIME).
2. Research Methodology
2.1. Using Pre-Trained Convolutional Networks
- Models are initiated with the pre-trained network without a fully connected (FC) layer;
- A new layer is added, containing “Maxpool” and “softmax” as activation functions and appended with the network’s primary architecture;
- The convolutional weights are kept frozen and only the new FC layers are trained with the new CNN architecture.
2.2. LIME as Explainable AI
3. Results
AUC-ROC Curve
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | Label | Train | Test | ||||
---|---|---|---|---|---|---|---|
Chest X-ray | CT scan | Total | Chest X-ray | CT scan | Total | ||
Mixed Data | COVID-19 | 486 | 160 | 646 | 122 | 40 | 162 |
Non-COVID-19 | 1266 | 160 | 1426 | 317 | 40 | 357 | |
Total | 1752 | 320 | 2072 | 439 | 80 | 519 |
Function | Value |
---|---|
Kernel size | 200 |
Maximum distance | 200 |
Ratio | 0.2 |
Algorithm | Accuracy (%) | Precision (%) | Recall (%) | F-1 Score (%) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
T | T | CI | T | T | CI | T | T | CI | T | T | CI | |
VGG16 | 95 | 91 | 93 ± 1.4 | 95 | 93 | 94 ± 1.3 | 95 | 91 | 93 ± 1.4 | 95 | 92 | 93.5 ± 1.34 |
InceptionResNetV2 | 94 | 93 | 93.5 ± 1.34 | 95 | 93 | 94 ± 1.3 | 94 | 93 | 93.5 ± 1.34 | 94 | 93 | 93.5 ± 1.35 |
ResNet50 | 88 | 85 | 86.5 ± 1.86 | 87 | 85 | 86 ± 1.89 | 88 | 85 | 86.5 ± 1.86 | 87 | 85 | 86 ± 1.89 |
MobileNetV2 | 99 | 91 | 95 ± 1.2 | 99 | 92 | 95.5 ± 1.13 | 99 | 91 | 95 ± 1.2 | 99 | 91 | 95 ± 1.2 |
ResNet101 | 88 | 86 | 87 ± 1.83 | 88 | 87 | 87.5 ± 1.80 | 88 | 86 | 87 ± 1.83 | 88 | 86 | 87 ± 1.83 |
VGG19 | 94 | 91 | 92.5 ± 1.43 | 94 | 92 | 93 ± 1.4 | 94 | 91 | 92.5 ± 1.43 | 94 | 92 | 93 ± 1.4 |
Dataset | Datasize | Model | Accuracy (%) |
---|---|---|---|
X-ray | 400 | VGG16 | 98.5 ± 1.191 |
MobileNetV2 | 98.5 ± 1.191 | ||
CT-Scan | 400 | MobileNetV2 | 94 ± 2.327 |
Mixed-data | 2591 | MobileNetV2 | 95 ± 1.12 |
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Ahsan, M.M.; Nazim, R.; Siddique, Z.; Huebner, P. Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME. Healthcare 2021, 9, 1099. https://doi.org/10.3390/healthcare9091099
Ahsan MM, Nazim R, Siddique Z, Huebner P. Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME. Healthcare. 2021; 9(9):1099. https://doi.org/10.3390/healthcare9091099
Chicago/Turabian StyleAhsan, Md Manjurul, Redwan Nazim, Zahed Siddique, and Pedro Huebner. 2021. "Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME" Healthcare 9, no. 9: 1099. https://doi.org/10.3390/healthcare9091099