Table 8 Performance analysis of fold-1 cross-validation.
From: Feature fusion context attention gate UNet for detection of polycystic ovary syndrome
Model | Accuracy | Precision | Recall | Specificity | F1-Score |
|---|---|---|---|---|---|
DenseNet | 68.9 | 68.5 | 68.1 | 69.3 | 68.3 |
VGG | 72.2 | 71.9 | 71.7 | 72.8 | 71.8 |
AlexNet | 73.4 | 73.0 | 72.9 | 73.8 | 72.9 |
ResNet | 77.0 | 76.7 | 76.3 | 77.5 | 76.5 |
U-Net | 79.2 | 78.9 | 78.4 | 79.6 | 78.6 |
Attention U-Net | 83.1 | 82.7 | 82.5 | 83.7 | 82.6 |
FCAU-Net | 99.9 | 99.9 | 99.8 | 99.9 | 99.9 |
