Table 6.
Comparison with state-of-the-art models.
| Existing methods | Dataset used (COVID-19 positive) |
Classes | Method used | Accuracy |
|---|---|---|---|---|
| Apostolopoulos and Mpesiana [7] | X-rays(1428) | Dataset 1- COVID-19, Pneumonia and Normal | Transfer learning | Dataset 1 - 93.48% |
| X-rays(1442) | Dataset 2- COVID-19, Pneumonia and Healthy | Dataset 2 - 94.72% | ||
| Jaiswal et al. [10] | CT-scans(2492) | COVID-19, Normal | DenseNet201 | 96.00% |
| Khan et al. [3] | X-rays(192) | Normal, Pneumonia, COVID-19 | DCNN | 95.00% |
| Jain et al. [4] | X-rays(6432) | Normal, COVID-19, Pneumonia | Transfer learning | 97.00% |
| Maghdid et al. [5] | CTs, and X-rays(431) | Normal, COVID-19 | Transfer learning | 98.00% |
| Wang et al. [14] | X-rays(13800) | Normal, COVID-19, Pneumonia | COVID-Net | 92.60% |
| Farooq at el.[15] | X-rays(5941) | COVID-19, Normal, Bacterial, Pneumonia, Viral | COVID-ResNet | 96.23% |
| Ozturk et al. [13] | X-rays(625) | Binary class: COVID-19, Normal | DarkCovidNet | Binary – 98.08% |
| X-rays(1125) | Multi-class: COVID-19, Normal, Pneumonia | Multi-class – 87.02% | ||
| Proposed (LMNet) | X-rays(6426) | COVID-19, Normal, Pneumonia | LMNet | 96.03% |
| Proposed (Ensemble) | X-rays(6426) | COVID-19, Normal, Pneumonia | LMNet DenseNet169 MobileNetV2 | 98.00% |