Table 8. Comparison with state-of-the-art method.
| Reference | Methodology | Classes | Samples count | Accuracy (%) |
|---|---|---|---|---|
| Tang et al. (2021) | EDL-COVID | COVID-19 | C:573, N:573 | 95.00 |
| – | – | Normal | C: 25, N: 25 | – |
| Narin, Kaya & Pamuk (2021) | ResNet-50 | COVID-19 | 100 | 98.00 |
| – | – | Normal | C: 50, N: 50 | – |
| Sethy & Behera (2020) | ResNet-50 and SVM | COVID-19 | 50 | 95.38 |
| – | – | Normal | C: 25, N: 25 | – |
| Togaçar, Ergen & Cömert (2020) | SqueezeNet and MobileNetV2 | COVID-19 | 458 | 98.25 |
| – | SMO and SVM | Normal | C: 295, N: 65, P: 98 | – |
| – | – | Pneumonia | – | – |
| Wang & Wong (2020) | COVID-Net | COVID-19 | 13800 | 92.60 |
| – | – | Normal | C: 183, N: –, P: – | – |
| – | – | Pneumonia | – | – |
| Ucar & Korkmaz (2020) | Bayes-SqueezeNet | COVID-19 | 5949 | 98.30 |
| – | – | Normal | C: 76, N: 1583, P: 4290 | – |
| – | – | Pneumonia | – | – |
| Farooq & Hafeez (2020) | COVID-ResNet | COVID-19 | 5941 | 96.23 |
| – | – | Normal | C: 68, N: –, BP: –, VP: – | – |
| – | – | Bacterial pneumonia | – | – |
| – | – | Viral pneumonia | – | – |
| Ozturk et al. (2020) | DarkCovidNet | COVID-19 | 625 | 98.08 |
| – | – | Normal | C: 125, N: 500 | – |
| – | – | COVID-19 | 1125 | 87.02 |
| – | – | Normal | C: 125, N: 500, P: 500 | – |
| – | – | Pneumonia | – | – |
| Nayak et al. (2021) | ResNet-34 | COVID-19 | 406 | 98.33 |
| Proposed method | DenseNet169 | Normal Pneumonia COVID-19 | C: 142 and P: 1739, N: 1739 | 98.66 and 83.75 |