Table 6.

ADA-COVID vs. pretrained models.

Reference	Data sources	No. of samples	Model	Performance
Ardakani et al. [84]	Real-time data from the hospital environment.	Total: 1,020 COVID-19 : 510 Non-COVID-19 : 510	AlexNet, VGG-16, VGG-19,…	Accuracy: 99.51 Recall: 100 Specificity: 99.02
Chen et al. [29]	Renmin Hospital of Wuhan University.	Total: 35,355	UNet++	Accuracy: 98.85 Recall: 94.34 Specificity: 99.16
Cifci [73]	Kaggle benchmark dataset [85]	Total: 5,800	AlexNet, Inception-V4	Accuracy: 94.74 Recall: 87.37 Specificity: 87.45
Javaheri et al. [36]	Five medical centers in Iran, SPIE-AAPM-NCI [86], LUNGx [87]	Total: 89,145 COVID-19 : 32,230 Non-COVID-19 : 56,915	BCDU-Net (U-Net)	Accuracy: 91.66 Recall: 87.5 Specificity: 94
Jin et al. [74]	Wuhan Union Hospital, LIDC-IDRI [88], ILD-HUG [89]	Total: 1,881 COVID-19 : 496 Non-COVID-19 : 1,385	ResNet152	Accuracy: 94.98 Recall: 94.06 Specificity: 95.47 F1: 92.78
Jin et al. [65]	Five different hospitals of China.	Total: 1,391 COVID-19 : 850 Non-COVID-19 : 541	DPN-92, Inception-v3, ResNet-50	Recall: 97.04 Specificity: 92.2
Li et al. [66]	Multiple hospitals environment.	Total: 4,536 COVID-19 : 1,296 Non-COVID-19 : 1,325	ResNet50	Recall: 90 Specificity: 96
Wu et al. [67]	China Medical University, Beijing Youan Hospital.	Total: 495 COVID-19 : 368 Non-COVID-19 : 127	ResNet50	Accuracy: 76 Recall: 81.1 Specificity: 61.5
Xu et al. [79]	Zhejiang University, Hospital of Wenzhou, Hospital of Wenling.	Total: 618 COVID-19 : 219 Non-COVID-19 : 399	ResNet18	Accuracy: 86.7 Recall: 81.5 F1: 81.1
Yousefzadeh et al. [75]	Real-time data from the hospital environment.	Total: 2,124 COVID-19 : 706 Non-COVID-19 : 1,418	DenseNet, ResNet, Xception, EcientNetB0	Accuracy: 96.4 Recall: 92.4 Specificity: 98.3 F1: 95.3
ADA-COVID	SARS-CoV-2 CT scan dataset	Total: 2,482 COVID-19 : 1,252 Non-COVID-19 : 1,229	ResNet50	Accuracy: 99.96 Recall: 99.80 Specificity: 99.80 F1: 99.90