Research Article
Classification of COVID-19 and Influenza Patients Using Deep Learning
Table 1
Comparison table of related work.
| | Author | Dataset | Disease | Technique | Accuracy (%) |
| | Kumar Nath et al. [1] | Chest X-ray and CT images | COVID-19 | Deep learning | X-ray images: 99.68; CT images: 71.81 | | Henzel et al. [17] | Questionnaires | COVID-19 | Machine learning | - | | Bernese et al. [18] | X-ray images | COVID-19 | Deep learning | 96.7 | | Goto et al. [19] | Viruses and cells | Influenza | HA cleavage | - | | Khan et al. [20] | Custom data | Influenza | Machine learning | 90 | | Yin et al. [21] | Time-series data of influenza | Influenza | Deep learning | 98-99 | | Guo, X., et al. [22] | Chest CT images | COVID-19 and influenza | Machine learning | 96.6 | | Taj et al. [24] | Time series data of influenza | COVID-19 | Deep learning, machine learning | - | | Hammoudi et al. [25] | Chest X-ray images | COVID-19 | Deep learning | 95.7 | | Kassania et al. [13] | X-ray and CT images of chest | COVID-19 | Deep learning | 98 | | Cabitza et al. [14] | Blood test data | COVID-19 | Machine learning | 90 | | Saygili et al. [26] | X-ray and CT images of chest | COVID-19 | Machine learning | 98 | | M. Ismael and Sengur [15] | Chest X-ray images | COVID-19 | Deep learning | 94.7 |
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