Fig. 3
From: Hand X-rays findings and a disease screening for Turner syndrome through deep learning model
The deep learning network structure based on a modified version of ResNet50. The network input size is 3*224*224, where 3 represents the number of channels and 224*224 represents the image size. The convolution calculation for each layer is shown in the diagram to the right of each convolution kernel, such as “64*112*112, k = 7, s = 2, p = 3”, where 64 represents the number of feature channels, 112*112 represents the feature size, k represents the convolution kernel size, s represents the convolution stride, and p represents the padding around the feature. The symbol “/2” in the diagram represents downsampling, which reduces the output feature size by half. There are a total of 50 layers in ResNet50, with the same bottlenecks shown schematically in the diagram