Table 14 Statistical significance testing on confusion matrix with FCE images.
From: Feature fusion context attention gate UNet for detection of polycystic ovary syndrome
Model | With FCE images (%) | ||||
|---|---|---|---|---|---|
Accuracy | Precision | Recall | F1-score | Misclassifications | |
DenseNet | 69.43 | 68.92 | 68.54 | 68.73 | 1,742 |
VGG | 72.62 | 72.40 | 72.01 | 72.20 | 1,561 |
AlexNet | 73.51 | 73.28 | 72.89 | 73.08 | 1,508 |
ResNet | 77.44 | 77.20 | 76.92 | 77.06 | 1,286 |
U-Net | 79.32 | 79.11 | 78.72 | 78.91 | 1,178 |
Attention U-Net | 83.78 | 83.50 | 83.19 | 83.34 | 926 |
Proposed FCAU-Net | 99.89 | 99.89 | 99.86 | 99.87 | 7 |
