Table 3. The summary of the COVID-19 diagnostic tools based on machine learning algorithms.
| Reference | Algorithm | Performance | Contribution | Benefit |
|---|---|---|---|---|
| Apostolopoulos & Mpesiana (2020) | Transfer Learning with CNN | Accuracy: 98.75% | Device approach for automatic diagnostic of COVID-19 based on X-ray | Cost-effective, fast diagnosis and reduce exposure of medical workers to COVID-19 |
| Sensitivity: 92.85% | ||||
| Specificity: 98.75% | ||||
| Ardakani et al. (2020) | Variants of CNN ResNet 101 |
Accuracy: 99.51% | Automated characterization and diagnosis of COVID-19 infection. | Cheaper and faster compared to the traditional laboratory analysis of COVID-19. Reduces medical worker’s workload. |
| Sensitivity: 100% | ||||
| Specificity: 99.02% | ||||
| Butt et al. (2020) | CNN | AUC: 0.996 | Outperform the traditional RT-PCR testing of COVID-19 | fast and reliable in detecting COVID-19 pandemic. |
| Sensitivity: 98.2% | ||||
| Specificity: 92.2%. | ||||
| Huang et al. (2020b) | Deep learning algorithm | Not Applicable as a result of ANOVA analysis | The assessment of the lung opacification measured is significantly different from the clinical groups | The approach has the potential to remove the subjectivity in the initial evaluation of COVID-19 findings as well as follow up pulmonary |
| Li et al. (2020b) | CNN | Sensitivity:87% | The automated framework that differentiates COVID-19 from pneumonia | Automated the COVID-19 testing process, reduces the testing time and fatigue. |
| Specificity:92% | ||||
| AUC: 95% | ||||
| Liu et al. (2020a) | P-CNN | Sensitivity: 100% | Diagnose COVID-19 with limited data and present new probabilistic Grad-CAM salient map | Limited amount of data can be used for the COVID-19 diagnoses, and it is interpretable. |
| Precision: 76.9% | ||||
| Ozturk et al. (2020) | CNN | Sensitivity: 85.35% | Improve efficiency and automate the COVID-19 screening process | Automate the process of COVID-19 diagnoses to reduce fatigue |
| Specificity: 92.18% | ||||
| Accuracy: 87.02% | ||||
| Salman et al. (2020) | CNN | Sensitivity: 100% | Automated COVID-19 screening process | Reduces diagnostic time |
| Specificity: 100% | ||||
| Accuracy: 100% | ||||
| Singh et al. (2020) | MODE based CNN | Sensitivity: > 90% | Diagnose COVID-19 with better accuracy than the competitive models | The proposal is beneficial to COVID-19 real-time classification |
| Specificity: > 90% | ||||
| Toğaçar, Ergen & Cömert (2020) | CNN-SVM | Overall accuracy: 99.27% | Contributed to the efficient detection of COVID-19 | Automated detection of COVID-19 patient |
| Ucar & Korkmaz (2020) | Bayesian-based SqueezeNet | Accuracy: 100% | Presents alternative rapid COVID-19 diagnostic tool based on deep Bayes-SqueezeNet | It will be of benefit to healthcare professionals in diagnosing COVID-19 efficiently. |
| Specificity: 99.67% | ||||
| Wu et al. (2020) | ResNet50 | Accuracy: 0.819 | Provide COVID-19 diagnosis from multiple view | Reduce the workload of a radiologist by offering fast and accurate COVID-19 diagnosis |
| Sensitivity: 0.760 | ||||
| Specificity: 0.811 | ||||
| Loey, Smarandache & Khalifa (2020) | Googlenet, Alexnet and RestNet18 | Googlenet, Alexnet and Googlenet scored 80.6%, 85.2% and 100% in the 4, 3 and 2 classes classification problems | Generate sufficient COVID-19 data to improve COVID-19 diagnosis | Early detection of COVID-19 and reduce the workload of radiologist |
| Hurt, Kligerman & Hsiao (2020) | Deep learning | Not provided | Improve the detection COVID-19 from X-ray | Early detection of COVID-19 |
| Jiang et al. (2020) | Machine learning algorithm | Accuracy: 70–80% | Detect COVID-19 severity in a patient at the initial presentation | Help in optimal utilization of scarce resources to cope with COVID-19 |
| Liu et al. (2020a) | Logistic regression | ROC: 0.93 | Applied CT quantification of pneumonia to predict progression to COVID-19 severity | provide a prognostic indicator for COVID-19 clinical management |
| Confidence interval: 95% | ||||
| Mei et al. (2020) | Deep ensemble algorithm | ROC: 0.92 | Predict COVID-19 with both image and none image clinical information | The ensemble diagnostic tool can detect COVID-19 patients rapidly |
| Accuracy: 68% | ||||
| Sensitivity: 84.3% | ||||
| Specificity: 82.8% | ||||
| Yang et al. (2020a) | Densely connected convolutional networks | Accuracy: 92% | Detect COVID-19 from CT scan via densely connected convolutional networks | Reduce radiologist workload |
| Sensitivity: 97% | ||||
| Specificity: 87% |