Table 2.

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 1: Normal, COVID-19, and typical viral pneumonia.

Method	Performance Metrics (%)
	Feature ExtractorLayer	Sensitivity (Recall)	Precision (PPV)	F1-score	Accuracy	Average Rank
$\text{ResNet}18$		96.40 (4)	96.35 (5)	96.37 (4)	96.17 (4)	4.3
$\text{ResNet}{18}^{+}$		97.60 (7)	98.06 (6)	97.82 (6)	97.52 (6)	6.3
$\text{ResNet}50$	Avg_pool	96.20 (7)	96.16 (6)	96.17 (6)	95.91 (6)	6.3
$\text{ResNet}{50}^{+}$		98.43 (3)	98.49 (4)	98.45 (4)	98.10 (4)	3.8
$\text{ResNetV}2$	Avg_pool	94.04 (10)	92.61 (10)	93.28 (11)	93.18 (10)	10.3
$\text{ResNetV}{2}^{+}$		97.48 (8)	97.07 (10)	97.26 (10)	97.01 (9)	9.3
$\text{Inception}$	pool5	96.40 (4)	96.40 (4)	96.35 (5)	96.01 (5)	4.5
${\text{Inception}}^{+}$		98.41 (4)	98.69 (3)	98.54 (3)	98.36 (3)	3.3
$\text{InceptionV}3$	Avg_pool	95.80 (8)	94.95 (9)	95.35 (8)	95.11 (8)	8.3
$\text{InceptionV}{3}^{+}$		98.26 (5)	98.10 (5)	98.18 (5)	97.75 (5)	5.0
$\text{Xception}$	Avg_pool	92.00 (11)	92.11 (11)	92.05 (10)	91.73 (11)	10.8
${\text{Xception}}^{+}$		97.24 (9)	97.34 (9)	97.29 (9)	96.98 (10)	9.3
$\text{DenseNet}201$	Avg_pool	97.48 (2)	97.66 (2)	97.56 (2)	97.30 (3)	2.3
$\text{DenseNet}{201}^{+}$		98.85 (2)	98.86 (2)	98.85 (2)	98.58 (2)	2.0
$\text{SqueezeNet}$	Pool10	95.14 (9)	95.13 (8)	95.06 (9)	94.76 (9)	8.8
${\text{SqueezeNet}}^{+}$		95.39 (11)	94.75 (11)	95.03 (11)	95.08 (11)	11.0
$\text{ShuffleNet}$	Node_200	96.34 (6)	95.68 (7)	96.00 (7)	95.82 (7)	6.8
${\text{ShuffleNet}}^{+}$		97.20 (10)	97.56 (8)	97.36 (8)	97.07 (8)	8.5
$\text{AlexNet}$	fc7	97.24 (3)	97.57 (3)	97.40 (3)	97.33 (2)	2.8
${\text{AlexNet}}^{+}$		97.67 (6)	97.84 (7)	97.75 (7)	97.39 (7)	6.8
$\text{VGGNet}19$	fc7	98.21(1)	98.43 (1)	98.32 (1)	98.10 (1)	1.0
$\text{VGGNet}{19}^{+}$		98.87 (1)	99.08 (1)	98.97 (1)	98.81 (1)	1.0
Avg. on CNN Avg on CNN+		95.93	95.73	95.81	95.58
		97.76	97.80	97.77	97.51

*Bold numbers indicate the best performance.