Table 13 Performance analysis of fold-5 cross-validation.
From: Feature fusion context attention gate UNet for detection of polycystic ovary syndrome
Model | Accuracy | Precision | Recall | Specificity | F1-Score |
|---|---|---|---|---|---|
DenseNet | 69.2 | 68.8 | 68.4 | 69.8 | 68.6 |
VGG | 72.5 | 72.1 | 71.9 | 73.1 | 72.0 |
AlexNet | 73.7 | 73.3 | 73.0 | 74.1 | 73.1 |
ResNet | 77.3 | 76.9 | 76.6 | 77.8 | 76.7 |
U-Net | 79.4 | 79.0 | 78.7 | 79.8 | 78.9 |
Attention U-Net | 83.5 | 83.1 | 82.8 | 84.0 | 83.0 |
FCAU-Net | 99.89 | 99.9 | 99.8 | 99.9 | 99.9 |
