TABLE 1.
Comparison of previous work in determining Pneumonia, COVID, and Normal Chest X‐ray Images
| References | Method used | Dataset utilized | Sensitivity | Accuracy | Specificity | Proposed approach | Summary |
|---|---|---|---|---|---|---|---|
| Proposed method | Train: Test = 3200:1600 | Total = 16 000, COVID = 3616, Non‐COVID = 12 384 | 98.5% | 99.1% | 98.95% | DenseNet‐201 | More Accuracy, Sensitivity and Specificity. Includes the GUI Tool |
| [38] | Train: Test = 2084:3100 | Total = 5148 Images (COVID‐184, Normal‐5000) | 98% | 90.89% | 87.1% | ResNet18, ResNet50, SqueezeNet and DenseNet‐121 | Results in very high on sensitivity; compares state‐of the‐ art‐ CNN models |
| [39] | 5 Fold Cross Validation | Total = 610 Images (COVID‐305, Normal ‐ 305) | 97.80% | 97.40% | 94.70% | Multiresolution CovXNet | CovXNet Proposed. Demonstartes high sensitivity, specificity, accuracy. Images used is low |
| [40] | 4 Fold Cross Validation | Total = 594 Images (COVID‐284, Normal‐310) | 97.5% | 95.3% | 98.60% | CoroNet (Xception) | Demonstrates high accuracy, sensitivity and specificity |
| [41] | Train: Test = 5467:965 | Total = 6432 Images (COVID‐576, Normal‐1583, Pneumonia ‐ 4273) | 92.7% | 95.3% | 98.2% | Inception V3, Xception, ResNeXt |
Comparison of state‐of‐the‐art CNN Models High accuracy, sensitivity, specificity |
| [35] | 5 Fold Cross Validation | Total = 6926 Images (Normal‐4337, COVID‐2589) | 92.35% | 94.43% | 96.33% | COVID X‐Net | High accuracy, sensitivity, specificity. Number of images is quite low |
| [42] | 10 Fold Cross Validation | Total = 1428 Images (Normal‐504, COVID‐224, Pneumonia ‐ 700) | 41% | 90.5% | 99% | VGG19, Inception, Xception, MobileNet v2, Resnetv2 |
Comparison of state‐of‐the‐art CNN Models High accuracy, sensitivity, specificity. Some error found in reporting data |
| [23] | 5 Fold Cross Validation | Total = 625 Images (COVID‐125, Normal‐500) | 95.13% | 98.08% | 95.30 | DarkNet | High accuracy, sensitivity, specificity. Number of images is low |
| [43] | Train: Test = 50:1 | Total = 13 975 Images (COVID‐5338, Normal‐8066) | 95% | 93.30% | 95% | COVIDNet | High accuracy, sensitivity, specificity |
| [34] | 5 Fold Cross Validation | Total = 1006 Images (COVID‐538, non‐COVID‐468) | 95.09% | 91.62% | 88.33% | Combining InceptionV3, Resnet50V2 and DenseNet201 | Ensemble based technique. High accuracy, sensitivity, specificity |