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Table 2 Architectures of basic CNN

From: Microaneurysm detection in fundus images using a two-step convolutional neural network

Layer Operation Input size Detail Berr, (p)
Layer 1 Input \(3\times 101\times 101\)
Layer 2 Convolutional \(16\times 96\times 96\) \(7\times 7\)
Layer 3 Max pooling \(16\times 48\times 48\) \(2\times 2\) 0.25
Layer 4 Convolutional \(16\times 44\times 44\) \(5\times 5\)
Layer 5 Max pooling \(16\times 22\times 22\) \(2\times 2\) 0.25
Layer 6 Convolutional \(16\times 20\times 20\) \(3\times 3\)
Layer 7 Max pooling \(16\times 10\times 10\) \(2\times 2\) 0.25
Layer 8 Fully connected 200 \(1\times 1\)
Layer 9 Fully connected 100 \(1\times 1\)
Layer 10 Fully connected 2 \(1\times 1\)