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Table 2 Network architecture of CSDAE

From: Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder

Layer (type) Output shape Param#
Input_1(InputLayer) (None, 512, 512, 1) 0
Conv2d_1(Conv2D) (None, 512, 512, 16) 160
Max_pooling2d_1(MaxPooling2D) (None, 256, 256, 16) 0
Conv2d_2(Conv2D) (None, 256, 256, 8) 1160
Max_pooling2d_2(MaxPooling2D) (None, 128, 128, 8) 0
Conv2d_3(Conv2D) (None, 128, 128, 8) 584
Max_pooling2d_3(MaxPooling2D) (None, 64, 64, 8) 0
Conv2d_4(Conv2D) (None, 64, 64, 8) 584
Up_sampling2d_1(UpSampling2D) (None, 128, 128, 8) 0
Conv2d_5(Conv2D) (None, 128, 128, 8) 584
Up_sampling2d_2(UpSampling2D) (None, 256, 256, 8) 0
Conv2d_6(Conv2D) (None, 256, 256, 8) 1168
Up_sampling2d_3(UpSampling2D) (None, 512, 512, 16) 0
Conv2d_7(Conv2D) (None, 512, 512, 1) 145