Table 5 Model summary.

From: RETRACTED ARTICLE: Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images

Tower Name

Name of Layers

Parameters

Output Shape

Description

Tower 1

Input_layer_T1

0

[(None,64,64,1)]

Input layer: [Input_layer_T1]

Output layer: [Maxpool_T1]

Conv2d_T1

1280

[(None,63,63,256)]

MaxPool_T1

0

[(None,31,31,256)]

Conv2d_T1_c

131,200

[(None,30,30,128)]

MaxPool_T1

0

[(None,15,15,128)]

Conv2d_T1

32,832

[(None,14,14,64)]

Maxpool_T1

0

[(None,7,7,64)]

Tower 2

Input_layer_T2

0

[(None,64,64,1)]

Input layer: [Input_layer_T2]

Output layer: [ Maxpool_T2]

Concatenation layer: [Conv2d_T2_c, Conv2d_T1_c]

Conv2d_T2

1280

[(None,63,63,256)]

MaxPool_T2

0

[(None,31,31,256)]

Conv2d_T2_c

131,200

[(None,30,30,128)]

Concat_T2

0

[(None,30,30,256)]

MaxPool_T2

0

[(None,15,15,256)]

Conv2d_T2

32,832

[(None,14,14,64)]

Maxpool_T2

0

[(None,7,7,64)]

Tower 3

Input_layer_T3

0

[(None,64,64,1)]

Input layer: [Input_layer_T3]

Output layer: [Maxpool_T3]

Concatenation layer: [Conv2d_T3_c,Conv2d_T2_c, Conv2d_T3_c]

Conv2d_T3

1280

[(None,63,63,256)]

MaxPool_T3

0

[(None,31,31,256)]

Conv2d_T3

131,200

[(None,30,30,128)]

Concat_T3

0

[(None,30,30,256)]

MaxPool_T3

0

[(None,15,15,256)]

Conv2d_T3

32,832

[(None,14,14,64)]

Maxpool_T3

0

[(None,7,7,64)]

Combined tower

Concat_T

0

[(None,7,7,192)]

Input layer: [Max_Pool_T1, Max_Pool_T2, Max_Pool_T3],

Output layer: [Dense]

Conv2d_T

24,608

[(None,6,6,32)]

MaxPool_T

0

[(None,3,3,32)]

Flatten_T

0

[(None,288)]

Dense_1

36,692

[(None,128)]

Dense_2

9030

[(None,70)]

Dense_3

142

[(None,2)]