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Fig. 2

image

Example of a CNN architecture: The input image undergoes a series of convolution layers into a series of feature maps. The first convolution transforms the 101 × 101 pixel image into four 101 × 101 pixel feature maps. To lower computation cost, max-pooling layers are used in between convolutions. They reduce the dimensionality of the image, dividing the size of the image by two. A fully connected layer then combines all feature maps for the classification.

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