Table 3 Confusion matrices for four classification model architectures on test images.
(a) ResNet50 | |||||
|---|---|---|---|---|---|
Predicted | |||||
Unfed | Fully fed | Semi-gravid | gravid | ||
Actual | Unfed | 71 | 0 | 0 | 0 |
Fully fed | 0 | 49 | 0 | 3 | |
Semi-gravid | 0 | 2 | 63 | 1 | |
Gravid | 0 | 0 | 0 | 45 | |
(b) MobileNetV2 | |||||
|---|---|---|---|---|---|
Predicted | |||||
Unfed | Fully fed | Semi-gravid | Gravid | ||
Actual | Unfed | 71 | 0 | 0 | 0 |
Fully fed | 0 | 45 | 1 | 6 | |
Semi-gravid | 1 | 3 | 58 | 4 | |
Gravid | 2 | 1 | 2 | 40 | |
(c) EfficientNet-B0 | |||||
|---|---|---|---|---|---|
Predicted | |||||
Unfed | Fully fed | Semi-gravid | Gravid | ||
Actual | Unfed | 71 | 0 | 0 | 0 |
Fully fed | 0 | 48 | 0 | 4 | |
Semi-gravid | 0 | 2 | 60 | 4 | |
Gravid | 2 | 0 | 3 | 40 | |
(d) ConvNeXtTiny | |||||
|---|---|---|---|---|---|
Predicted | |||||
Unfed | Fully fed | Semi-gravid | Gravid | ||
Actual | Unfed | 65 | 1 | 0 | 5 |
Fully fed | 0 | 48 | 2 | 2 | |
Semi-gravid | 3 | 1 | 52 | 10 | |
Gravid | 1 | 2 | 4 | 38 | |
