Research Article

An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques

Table 3

AlexNet: deep neural network layers.

Layer (type)Output shapeParameter #

conv2d_13 (Conv2D)(None, 54, 54, 96)34944
activation (activation)(None, 54, 54, 96)0
max_pooling2d_5 (MaxPooling2)(None, 27, 27, 96)0
conv2d_14 (Conv2D)(None, 17, 17, 256)2973952
activation_1 (activation)(None, 17, 17, 256)0
max_pooling2d_6 (MaxPooling2)(None, 8, 8, 256)0
conv2d_15 (Conv2D)(None, 6, 6, 384)885120
activation_2 (activation)(None, 6, 6, 384)0
conv2d_16 (Conv2D)(None, 4, 4, 384)1327488
activation_3 (activation)(None, 4, 4, 384)0
conv2d_17 (Conv2D)(None, 2, 2, 256)884992
activation_4 (activation)(None, 2, 2, 256)0
max_pooling2d_7 (MaxPooling2)(None, 1, 1, 256)0
flatten_1 (flatten)(None, 256)0
dense_3 (dense)(None, 4096)1052672
activation_5 (activation)(None, 4096)0
dropout (dropout)(None, 4096)0
dense_4 (dense)(None, 4096)16781312
activation_6 (activation)(None, 4096)0
dropout_1 (dropout)(None, 4096)0
dense_5 (dense)(None, 1000)4097000
activation_7 (activation)(None, 1000)0
dropout_2 (dropout)(None, 1000)0
dense_6 (dense)(None, 3)3003
Total params: 28040483; trainable parameters: 28040483; nontrainable parameters: 0