Table 3 Performance of 4 different CNN models.
From: An improved CNN model in image classification application on water turbidity
No | AI model | Data source | CNN structure | Run time | Accuracy | Performance |
|---|---|---|---|---|---|---|
M1 | CNN8_Layer | 10 classes of water quality turbidity images, 25 for each class | Figure 7 | 65s | 88.0% | Figure 10(a) |
M2 | CNN8 + Drop | 67s | 84.0% | Figure 10(c) | ||
M3 | CNN10_Layer | 145s | 88.0% | Figure 10(e) | ||
M4 | CNN10 + Drop | 149s | 86.0% | Figure 10(g) | ||
MN1 | CNN8_Layer | 10 classes of water quality turbidity images, including 4 noise, joint noise, 100 pictures per class | 266s | 94.0% | Figure 10(b) | |
MN2 | CNN8 + Drop | 269s | 93.5% | Figure 10(d) | ||
MN3 | CNN10_Layer | 675s | 95.0% | Figure 10(f) | ||
MN4 | CNN10 + Drop | 687s | 96.5% | Figure 10(h) |
