Table 4 Evaluation metric of DBAR_Net and transfer learning model.
From: An attention enhanced dilated bottleneck network for kidney disease classification
Model | Kidney class | Precision | Recall | F1-score | Accuracy (%) |
|---|---|---|---|---|---|
VGG16 | Cyst | 0.81 | 0.75 | 0.78 | 97.50 |
Stone | 0.85 | 0.57 | 0.68 | ||
Kidney | 0.56 | 0.98 | 0.72 | ||
Tumor | 0.98 | 0.67 | 0.78 | ||
VGG19 | Cyst | 0.98 | 0.99 | 0.98 | 97.44 |
Stone | 0.97 | 0.94 | 0.96 | ||
Kidney | 0.97 | 0.97 | 0.97 | ||
Tumor | 0.97 | 0.98 | 0.98 | ||
ResNet50 | Cyst | 0.98 | 0.98 | 0.98 | 96.90 |
Stone | 0.96 | 0.94 | 0.95 | ||
Kidney | 0.95 | 0.96 | 0.96 | ||
Tumor | 0.97 | 0.98 | 0.98 | ||
Xception | Cyst | 0.98 | 0.98 | 0.98 | 97.26 |
Stone | 0.97 | 0.94 | 0.95 | ||
Kidney | 0.96 | 0.97 | 0.97 | ||
Tumor | 0.97 | 0.98 | 0.98 | ||
Mobile Net | Cyst | 0.99 | 0.98 | 0.98 | 97.22 |
Stone | 0.98 | 0.95 | 0.96 | ||
Kidney | 0.97 | 0.98 | 0.98 | ||
Tumor | 0.98 | 0.99 | 0.98 | ||
InceptionV3 | Cyst | 0.99 | 0.97 | 0.98 | 96.90 |
Stone | 0.96 | 0.93 | 0.94 | ||
Kidney | 0.95 | 0.97 | 0.96 | ||
Tumor | 0.97 | 0.98 | 0.98 | ||
EfficientNetB0 | Cyst | 0.96 | 0.99 | 0.97 | 97.26 |
Stone | 0.98 | 0.94 | 0.96 | ||
Kidney | 0.97 | 0.98 | 0.97 | ||
Tumor | 0.98 | 0.98 | 0.98 | ||
Proposed DBAR_Net | Cyst | 0.98 | 0.98 | 0.98 | 98.86 |
Stone | 0.98 | 0.94 | 0.96 | ||
Kidney | 0.96 | 0.97 | 0.96 | ||
Tumor | 0.97 | 0.98 | 0.97 |
