Table 9.

Comparison of the results (%) attained with CoviWavNet and those of the related studies based on the SARS-COV-2-CT-Scan dataset.

Article	Method	Acc	SE	SP	F1-score	Prec
[83]	Customized CNN	95.00	96.00	–	95.00	95.00
[48]	CoviDenseNet	86.88	87.41	85.92	89.53	91.76
[52]	x-DNN3	97.38	95.53	–	97.31	99.00
[86]	Customized Simple CNN	95.78	96.00	95.56	–	–
[82]	Bi-LSTM	98.37	98.87	–	98.14	98.74
[47]	VGG-16+ResNet-50+Xception+Majority voting	98.79	98.79	98.79	98.79	98.79
[85]	Fuzzy Ranking + VGG-11, ResNet-50-2, and Inception v3	98.93	99.08	99.00	98.93	98.93
[84]	Customized CNN+Genetic Algorithm+XBoost	99.00	99.00	–	99.00	99.10
[43]	ResNet18+ShuffleNet+AlexNet+ GoogleNet+DWT+GLCM+ Statistical features+PCA+SVM	99.00	99.00	99.00	99.00	99.00
Proposed	ResNet-50 trained with DWT heatmaps and CT images + C-SVM	99.62	99.54	99.69	99.62	99.70

${}^3$x-DNN is an explainable deep neural network.