Table 7.

ADA-COVID vs. customized models.

Reference	Data sources	No. of samples	Model	Performance
Amyar et al. [13]	COVID CT [16], COVID-19 CT segmentation dataset [90], Henri Becquerel Center	Total: 1,044 COVID-19 : 449 Non-COVID-19 : 595	Encoder-decoder with multilayer perceptron	Accuracy: 86.0 Recall: 94.0 Specificity: 79.0
Elghamrawy and Hassanien. [80]	COVID-19 database [91], COVID CT [16]	Total: 583 COVID-19 : 432 Non-COVID-19 : 151	WOA-CNN	Accuracy: 96.40 Recall: 97.25 Precision: 97.3
Farid et al. [78]	Kaggle benchmark dataset [85]	Total: 102 COVID-19 : 51 Non-COVID-19 : 51	CNN	Accuracy: 94.11 Precision: 99.4 F1: 94.0
Hasan et al. [58]	COVID-19 [92], SPIE-AAPM-NCI lung nodule classification challenge dataset [86]	Total: 321 COVID-19 : 118 Non-COVID-19 : 203	QDE–DF	Accuracy: 99.68
He et al. [11]	COVID-19 database [91], COVID-19 [92], Eurorad [93], corona cases [94]	Total: 746 COVID-19 : 349 Non-COVID-19 : 397	CRNet	Accuracy: 86.0 F1: 85.0 AUC: 94.0
Liu et al. [68]	Ten designated COVID-19 hospitals in China	Total: 1,993 COVID-19 : 920 Non-COVID-19 : 1,073	Modified DenseNet-264	Accuracy: 94.3 Recall: 93.1 Specificity: 95.1
Singh et al. [69]	COVID-19 patient chest CT images [95]	Total: 150 COVID-19 : 75 Non-COVID-19 : 75	MODE-CNN	Accuracy: 93.25 Recall: 90.70 Specificity: 90.72
Wang et al. [77]	Xi'an Jiaotong University, Nanchang University, Xi'an Medical College	Total: 1,065 COVID-19 : 740 Non-COVID-19 : 325	Modified inception	Accuracy: 79.3 Recall: 83.0 Specificity: 67.0
Song et al. [50]	Hospital of Wuhan University, third affiliated hospital	Total: 1,990 COVID-19 : 777 Non-COVID-19 : 1,213	DRE-Net	Accuracy: 94.3 Recall: 93.0 Precision: 96.0
Zheng et al. [70]	Union Hospital, Tongji Medical College, Huazhong University of Science and Technology	Total: 630	DeCoVNet	Accuracy: 90.1 Recall: 90.7 Specificity: 91.1
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