Table 3 Performance comparison for Covid-19 detection
From: COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images
Model | Data type | Number of classes | Image size | Accuracy (%) | Sensitivity (Recall) (%) | Specificity (%) | Precision (%) | F1 score |
|---|---|---|---|---|---|---|---|---|
COVID-XNet [2] | X-ray | 2 | 128 × 128 | 94.43 | 92.53 | 96.33 | 93.76 | 93.14 |
COVIDGAN [17] | X-ray | 2 | 112 × 112 | 95 | 90 | 97.00 | 96% | --- |
COVID-CAPS [15] | X-ray | 2 | 224 × 224 | 95.7 | 90 | 95.80 | --- | --- |
ResNet50 [1] | X-ray | 2 | 224 × 224 | 96.1 | 91.8 | 96.6 | 76.5 | 83.5 |
MobileViT | X-ray | 2 | 128 × 128 | 92.5 | 97.00 | 88.00 | 89.00 | 92.8 |
MobileNetV3 | X-ray | 2 | 128 × 128 | 97 | 97.50 | 96.50 | 96.53 | 97 |
COV-MobNets (Proposed) | X-ray | 2 | 128 × 128 | 97.75 | 98.50 | 97.00 | 97. 04 | 97.78 |