Table 1.
Comparison of different models of neural networks trained for 100 epochs
| Model Name | Classification Accuracy | Recall | Specificity | Precision |
|---|---|---|---|---|
| ResNeXt | ||||
| Augmented | 89.38% (+4.32%) | 0.80 (+0.09) | 0.99 (-0.01) | 0.99 (=) |
| Original | 85.06% | 0.71 | 1.00 | 0.99 |
| ConvNet | 87.50% (+2.53%) | 0.84 (-0.03) | 0.91 (+0.08) | 0.90 (+0.07) |
| 84.97% | 0.87 | 0.83 | 0.83 | |
| CNN8 | 85.16% (+0.38%) | 0.82 (+0.03) | 0.88 (-0.02) | 0.88 (-0.01) |
| 84.78% | 0.79 | 0.90 | 0.89 | |
| ResNet-v2 | 82.78% (-1.35%) | 0.76 (-0.02) | 0.9 (=) | 0.88 (-0.01) |
| 84.13% | 0.78 | 0.90 | 0.89 | |
| CNN6 | 82.00% (+1.50%) | 0.82 (+0.12) | 0.821 (-0.09) | 0.82 (-0.06) |
| 80.50% | 0.70 | 0.9 | 0.88 | |
| GoogLeNet | 81.38% (+1.35%) | 0.70 (-0.15) | 0.92 (+0.17) | 0.90 (+0.13) |
| 80.03% | 0.85 | 0.75 | 0.77 | |
| AlexNet | 80.66% (+1.28%) | 0.69 (-0.13) | 0.93 (+0.16) | 0.90 (+0.13) |
| 79.38% | 0.82 | 0.77 | 0.78 | |
| CNN2 | 68.22% (-11.03%) | 0.82 (+0.07) | 0.55 (-0.29) | 0.64 (-0.18) |
| 79.25% | 0.75 | 0.84 | 0.82 | |
| Extended training - number of epochs n - Augmented dataset | ||||
| ResNeXt150 | 90.03% | 0.81 | 0.99 | 0.99 |
| ConvNet75 | 90.00% | 0.84 | 0.96 | 0.96 |
| Inception v3200 | 88.75% | 0.78 | 0.99 | 0.99 |
| ConvNet150 | 87.66% | 0.77 | 0.99 | 0.98 |
| Object detection algorithm - Augmented dataset | ||||
| Combined | 96.00% | 0.94 | 0.98 | 0.98 |
| MobileNet | 89.84% | 0.81 | 0.99 | 0.99 |
| Inception-v2 | 86.41% | 0.77 | 0.96 | 0.95 |
Results using Augmented and Original dataset are shown in two lines, (variation in performances shown in parenthesis). ResNeXt and ConvNet were also trained for “n” different number of epochs, indicated as e.g.: ResNeXtn. Inception v3 indicates the attempt of retraining a publicly available network. “Combined” indicates a combination of MobileNet and Inception-v2.