Skip to main content
. 2022 Apr 4;151:178–189. doi: 10.1016/j.neunet.2022.03.034

Table 1.

The comparison of performances of the models while experimented over the dataset of CT images.

Base Metric Quasi-
ProtoPNet
Ps-ProtoPNet
(Singh & Yow, 2021b)
Gen-ProtoPNet
(Singh & Yow, 2021a)
NP-ProtoPNet
(Singh & Yow, 2021c)
ProtoPNet
(Chen et al., 2018)
Base only
VGG-16 Accuracy 99.05 98.83 95.85 98.23 90.84 99.03
Precision 0.98 0.96 0.93 0.93 0.89 0.98
Recall 0.99 0.98 0.95 0.95 0.91 0.99
F1-score 0.98 0.97 0.94 0.94 0.90 0.98

VGG-19 Accuracy 99.15 98.53 98.17 98.23 96.54 98.71
Precision 0.98 0.97 0.95 0.91 0.93 0.98
Recall 0.99 0.99 0.99 0.96 0.95 0.99
F1-score 0.98 0.98 0.97 0.93 0.94 0.98

ResNet-34 Accuracy 99.29 ± 
0.04
98.97 ± 
0.05
98.40 ± 
0.12
98.45 ± 
0.07
97.05 ± 
0.06
99.24 ± 
0.10
Precision 0.99 0.97 0.96 0.96 0.95 0.99
Recall 0.99 0.99 0.99 0.99 0.96 0.99
F1-score 0.99 0.98 0.97 0.97 0.96 0.99

ResNet-152 Accuracy 99.26 ± 
0.05
98.85 ± 
0.04
95.90 ± 
0.09
98.48 ± 
0.06
88.20 ± 
0.08
99.40 ± 
0.05
Precision 0.98 0.97 0.93 0.99 0.87 0.99
Recall 0.99 0.98 0.93 0.99 0.87 0.99
F1-score 0.98 0.97 0.93 0.99 0.87 0.99

DenseNet-121 Accuracy 99.44 ± 
0.04
99.24 ± 
0.05
98.97 ± 
0.02
98.83 ± 
0.10
98.81 ± 
0.07
99.32 ± 
0.03
Precision 0.99 0.98 0.98 0.99 0.98 0.99
Recall 0.99 0.99 0.99 0.98 0.98 0.99
F1-score 0.99 0.98 0.98 0.98 0.98 0.99

DenseNet-161 Accuracy 99.37 ± 
0.02
99.02 ± 
0.03
98.87 ± 
0.02
98.88 ± 
0.03
98.76 ± 
0.07
99.41 ± 
0.07
Precision 0.98 0.96 0.98 0.97 0.97 0.99
Recall 0.99 0.99 0.99 0.99 0.99 0.99
F1-score 0.99 0.97 0.98 0.97 0.98 0.99