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Fig. 3 | BMC Urology

Fig. 3

From: Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray

Fig. 3

ResNet architecture. The patches were input and convoluted as they passed through each layer. Each box indicates the number (n) and size (length (l) × width (w) = pixels) of images in each layer. The computer’s prediction of whether an input patch was included was output and each loss was calculated if the output was not concordant with the input. The parameters were optimized using the back propagation method, in which each loss was supposed to be minimized

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