Figure 2 | Scientific Reports

Figure 2

From: Classification of ischemia from myocardial polar maps in 15O–H2O cardiac perfusion imaging using a convolutional neural network

Figure 2

The 2D CNN architecture used in this study. The input polar map is a 256 × 256 RGB image. Each 2D convolutional layer contains a 3 × 3 kernel with stride 2 × 2 and ReLU as activation function. Filter sizes are increased gradually from 12, 16, 32 to 64. Each convolutional layer is followed by a max pooling layer with a window size of 2 × 2. Thus, the output of the last convolution and max-pooling layer is reduced to 1 × 1 × 64, which is given as an input to the flatten layer. The last layers contain a flatten layer of size 64 followed by two dense layer of sizes 512 and 128, using ReLU as activation function. The output layer is a dense layer with 1 output (0 or 1) with a sigmoid function. The input size of each convolutional layer and the flatten layer is given above each layer. For the fully connected layers, the output size is given. conv = 2D convolutional layer, flatten = flatten layer, fc = fully connected layer.

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