Table 7.
Comparison of our model with the state-of-the-art methods. Note that “PneumoniaV” and “PneumoniaB” denote the disease “Viral Pneumonia” and “Bacterial Pneumonia” respectively.
| Ref | Cases | Classes | Method | Accuracy (%) | XAI | Params. (in Millions) |
|---|---|---|---|---|---|---|
| [20] | TB = 4248 Normal = 453 |
2 | Transfer learning with AlexNet and GoogLeNet | 85.68 | No | AlexNet = 61 GoogleNet = 7 |
| [33] | Normal = 8851 Covid19 = 180 Pneumonia = 6054 |
3 | Ensemble of Xception and ResNet50 | 91.40 | No | Xception Net = 22 ResNet = 11 |
| [52] | Normal = 310 PneumoniaB = 330 PneumoniaV = 327 COVID-19 = 284 |
4 | CNN-based CoroNet | 89.60 | No | CNN = 33.97 |
| [30] | Normal = 1583 COVID-19 = 576 Pneumonia = 4273 TB = 155 |
4 | Custom CNN | 94.53 | No | CNN = 34.73 |
| [15] | Normal = 310 PneumoniaB = 330 PneumoniaV = 327 COVID-19 = 284 |
4 | Attention based VGG | 85.43 | No | VGG-16 = 18 VGG-19 = 21.2 |
| [32] | Normal = 1341 COVID-19 = 864 Pneumonia = 1345 |
3 | Inception V3 with Transfer learning | 93.00 | Yes | Binary Class = 23.8 Multiclass = 23.8 |
| [36] | Normal = 439 COVID-19 = 435 PneumoniaB = 439 PneumoniaV = 439 TB = 434 |
5 | Transfer learning with Resnet18 | 91.60 | No | Resnet18 = 11 |
| Ours | Normal = 1583 Covid19 = 576 Pneumonia = 4273 TB = 700 |
4 | Custom CNN | 95.94 | Yes | CNN = 3.7 |