Table 3.

Comparison of deep CNNs as an end-2-end Network and as a feature extractor along with the proposed LMPL classifier $({\text{CNN}}^{+}$) in Experiment 2: COVID-19, SARS, and MERS pneumonia.

Method	Performance Metrics (%)
	Feature ExtractorLayer	Sensitivity (Recall)	Precision (PPV)	F1-score	Accuracy	Average Rank
$\text{ResNet}18$		87.47 (5)	84.52 (7)	85.86 (5)	88.02 (4)	5.3
$\text{ResNet}{18}^{+}$		91.97 (8)	90.66 (7)	91.22 (7)	92.72 (7)	7.3
$\text{ResNet}50$	Avg_pool	88.11 (4)	84.90 (6)	86.35 (4)	87.87 (5)	4.8
$\text{ResNet}{50}^{+}$		94.25 (6)	91.26 (5)	92.64 (5)	93.58 (5)	5.3
$\text{ResNetV}2$	Avg_pool	73.10 (11)	76.76 (9)	74.56 (11)	78.74 (10)	10.3
$\text{ResNetV}{2}^{+}$		91.45 (10)	88.99 (9)	90.12 (8)	91.58 (9)	9.0
$\text{Inception}$	pool5	83.76 (8)	85.38 (5)	84.38 (8)	86.59 (8)	7.3
${\text{Inception}}^{+}$		95.68 (3)	94.35 (3)	94.95 (3)	95.72 (3)	3.0
$\text{InceptionV}3$	Avg_pool	80.70 (9)	76.38 (10)	77.95 (9)	80.46 (9)	9.3
$\text{InceptionV}{3}^{+}$		92.60 (7)	90.90 (6)	91.70 (6)	93.15 (6)	6.3
$\text{Xception}$	Avg_pool	75.04 (10)	74.49 (11)	74.75 (10)	78.60 (11)	10.5
${\text{Xception}}^{+}$		95.46 (4)	89.00 (8)	90.10 (9)	91.73 (8)	7.3
$\text{DenseNet}201$	Avg_pool	91.83 (3)	91.60 (2)	91.57 (2)	92.87 (2)	2.3
$\text{DenseNet}{201}^{+}$		96.59 (2)	96.20 (2)	96.39 (2)	96.72 (2)	2.0
$\text{SqueezeNet}$	Pool10	85.64 (6)	84.51 (8)	85.02 (7)	87.30 (7)	7.0
${\text{SqueezeNet}}^{+}$		85.39 (11)	86.12 (11)	85.45 (11)	87.87 (11)	11.0
$\text{ShuffleNet}$	Node_200	84.68 (7)	85.95 (4)	85.27 (6)	87.73 (6)	5.8
${\text{ShuffleNet}}^{+}$		91.80 (9)	88.71 (10)	90.10 (9)	91.16 (10)	9.5
$\text{AlexNet}$	fc7	92.08 (2)	91.07 (3)	91.39 (3)	92.72 (3)	2.8
${\text{AlexNet}}^{+}$		94.87 (5)	91.99 (4)	93.33 (4)	94.15 (4)	4.3
$\text{VGGNet}19$	fc7	92.32 (1)	92.58 (1)	92.45 (1)	93.30 (1)	1.0
$\text{VGGNet}{19}^{+}$		96.64 (1)	96.29 (1)	96.46 (1)	96.86 (1)	1.0
Avg. on CNN Avg. on CNN+		85.88	84.38	84.50	86.75
		93.34	91.32	92.04	93.20

*Bold numbers indicate the best performance.