Table 1.

Summarization of deep learning applications for medical imaging-based COVID-19 diagnosis

Authors	Sample size	Methods	Results
Khan et al. [19]	1300 chest X-rays including 290 COVID-19 cases	CoroNet: Xception architecture	Accuracy = 89.6%
Wu et al. [20]	495 CT images consisting of 368 COVID-19 cases	Multi-view fusion model using deep learning techniques	Accuracy = 70.0% AUC = 73.2%
Wang and Wong [21]	13,975 X-ray images from 13,870 patients	COVID-Net: deep CNN architecture	Accuracy = 92.4%
Wang et al. [22]	325 CT scans of COVID-19 and 740 of pneumonia	Deep transfer learning using modified Inception	Accuracy = 79.3% Specificity = 83.0%
Butt et al. [23]	618 CT images including 219 COVID-19 cases	3D deep learning with location-attention mechanism	Accuracy = 86.7%
Jin et al. [24]	970 CT images from 496 patients	Deep neural network	Accuracy = 94.98% Specificity = 95.47%
Song et al. [25]	275 CT scans comprising of 88 COVID-19 cases	DeepPneumonia: ResNet architecture	AUC = 99.0% Sensitivity = 93.0%
Islam et al. [27]	421 X-ray images including 141 COVID-19 cases	Combined deep CNN-LSTM architecture	Accuracy = 97% Specificity = 91%