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. 2020 Sep 12;51(2):1010–1021. doi: 10.1007/s10489-020-01867-1

Table 2.

Class-wise precision performance comparison with other deep learning techniques in literature with our findings for COVID-19 detection

Backbone Accuracy COVID-19 Normal Pneumonia
Concurrent proposed approach:
VGG16 [16] 0.77 0.636
COVIDNet-CXR Small [23] 0.964 0.898 0.947
Flat - EfficientNetB0 [16] 0.90 1.0
Flat - EfficientNetB3 [16] 0.939 1.0
COVIDNet-CXR Large [23] 0.943 0.909 0.917 0.989
COVIDNet-CXR3-A[23] 0.979 0.921 0.903
ResNet18 [2] 0.951 0.918 0.943
Our results:
VGG16 (v1 Augmentation) 0.88 0.82 0.84 0.98
VGG16 (v2 GAN Augmentation) 0.90 0.93 0.87 0.96
Resnet50 (v2 Augmentation) 0.943 0.97 0.93 0.96
EfficientNetB0 (v2 Augmented) 0.968 1.0 0.96 0.96