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. 2022 Apr 25;2022:2014349. doi: 10.1155/2022/2014349

Table 4.

Sensitivity, specificity, precision, and F1-score comparisons between the baseline VGG16, FP-VGG16, and OSFP-Net. The best results are highlighted in bold. The listed metrics were obtained on the test dataset.

Fold Method Metrics (%)
SEN SPE PRE F1-score
1 VGG16 79.5 83.0 66.0 72.1
FP-VGG16 87.5 100.0 100.0 93.3
OSFP-Net 95.8 95.2 92.0 93.9
2 VGG16 74.0 85.5 75.5 74.7
FP-VGG16 80.0 95.7 88.9 84.2
OSFP-Net 95.0 95.7 90.5 92.7
3 VGG16 81.6 90.5 83.3 82.5
FP-VGG16 80.0 100.0 100.0 88.9
OSFP-Net 90.0 100.0 100.0 94.7
4 VGG16 84.7 89.2 86.2 85.5
FP-VGG16 95.7 90.7 84.6 89.8
OSFP-Net 100.0 100.0 100.0 100.0
5 VGG16 88.9 91.8 80.0 84.2
FP-VGG16 93.1 100.0 100.0 96.4
OSFP-Net 100.0 97.3 96.7 98.3
Avg. ±Std. VGG16 81.8 ± 5.0 88.0 ± 3.3 78.2 ± 7.1 79.8 ± 5.4
FP-VGG16 88.4 ± 6.8 97.3 ± 3.7 94.7 ± 6.6 91.2 ± 4.7
OSFP-Net 96.2 ± 3.7 97.6 ± 2.0 95.8 ± 4.0 95.9 ±  2.8