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Table 1.
Architecture of the convolutional neural network.
| No | Layer | Size output |
|---|---|---|
| 1 | Input | 131 × 131 × 1 |
| 2 | Conv2D | 129 × 129 × n |
| 3 | MaxPooling2D | 64 × 64 × n |
| 4 | Conv2D | 62 × 62 × 2n |
| 5 | MaxPooling2D | 31 × 31 × 2n |
| 6 | Conv2D | 29 × 29 × 2n |
| 7 | MaxPooling2D | 14 × 14 × 2n |
| 8 | Conv2D | 12 × 12 × 2n |
| 9 | MaxPooling2D | 6 × 6 × 2n |
| 10 | Conv2D | 4 × 4 × 2n |
| 11 | MaxPooling2D | 2 × 2 × 2n |
| 12 | Flatten | 8n |
| 13 | Dropout | 8n |
| 14 | Dense | 8n |
| 15 | Dense | 1 |
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