Table 9 Performance analysis of fold-2 cross-validation.
From: Feature fusion context attention gate UNet for detection of polycystic ovary syndrome
Model | Accuracy | Precision | Recall | Specificity | F1-Score |
|---|---|---|---|---|---|
DenseNet | 69.4 | 68.9 | 68.6 | 70.1 | 68.7 |
VGG | 72.7 | 72.2 | 72.0 | 73.3 | 72.1 |
AlexNet | 73.8 | 73.4 | 73.1 | 74.2 | 73.2 |
ResNet | 77.5 | 77.2 | 76.8 | 78.0 | 77.0 |
U-Net | 79.5 | 79.1 | 78.8 | 79.9 | 78.9 |
Attention U-Net | 83.6 | 83.3 | 82.9 | 84.1 | 83.1 |
FCAU-Net | 99.8 | 99.8 | 99.7 | 99.9 | 99.8 |
