From: Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms
Model | Classifier | Performance metrics (%) | |||
---|---|---|---|---|---|
Accuracy | Recall | Precision | F1 score | ||
DenseNet201 | RF | 99.44 | 99.44 | 99.45 | 99.44 |
SVM | 96.48 | 96.48 | 96.50 | 96.50 | |
DT | 93.61 | 93.61 | 93.69 | 93.60 | |
KNN | 100 | 100 | 100 | 100 | |
LGR | 99.16 | 99.16 | 99.17 | 99.16 | |
ResNet50 | RF | 98.14 | 98.14 | 98.17 | 98.15 |
SVM | 89.9 | 89.9 | 90.29 | 89.88 | |
DT | 92.77 | 92.77 | 92.84 | 92.77 | |
KNN | 99.81 | 99.81 | 99.81 | 99.81 | |
LGR | 98.24 | 98.24 | 98.24 | 98.24 | |
Xception | RF | 94.16 | 94.16 | 94.21 | 94.16 |
SVM | 89.53 | 89.53 | 89.68 | 89.52 | |
DT | 85 | 85 | 85 | 84.98 | |
KNN | 99.62 | 99.62 | 99.63 | 99.62 | |
LGR | 97.59 | 97.59 | 97.59 | 97.59 | |
VGG16 | RF | 99.07 | 99.07 | 99.08 | 99.08 |
SVM | 90.27 | 90.27 | 90.44 | 90.26 | |
DT | 93.14 | 93.14 | 93.15 | 93.14 | |
KNN | 99.81 | 99.81 | 99.81 | 99.81 | |
LGR | 95.55 | 95.55 | 95.57 | 95.55 |