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Table 1 Architectures of final CNN with different input patch-sizes based on trial and error

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 \(16\times 101\times 101\) \(7\times 7\)
Layer 3 Max pooling \(16\times 50\times 50\) \(2\times 2\) 0.25
Layer 4 Convolutional \(16\times 48\times 48\) \(5\times 5\)
Layer 5 Max pooling \(16\times 24\times 24\) \(2\times 2\)
Layer 6 Convolutional \(16\times 22\times 22\) \(3\times 3\)
Layer 7 Max pooling \(16\times 11\times 11\) \(2\times 2\) 0.25
Layer 8 Convolutional \(16\times 10\times 10\) \(2\times 2\)
Layer 9 Max pooling \(16\times 5\times 5\) \(2\times 2\)
Layer 10 Convolutional \(16\times 4\times 4\) \(2\times 2\)
Layer 11 Max pooling \(16\times 2\times 2\) \(2\times 2\)
Layer 12 Fully connected 100 \(1\times 1\)
Layer 13 Fully connected 2 \(1\times 1\)
  1. Berr,(p) is the probability of Bernoulli distribution