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Table 1 Structure and parameters of DC-ResNet

From: 3cDe-Net: a cervical cancer cell detection network based on an improved backbone network and multiscale feature fusion

Improved backbone: DC-ResNet

\(7 \times 7\), 64, stride 2

\(3 \times 3\), max pooling, stride 2

Residual group convolution

\(\left[ {\begin{array}{*{20}c} {1 \times 1} & {128} \\ {3 \times 3} & {128} \\ {1 \times 1} & {256} \\ \end{array} } \right] \times 3\)

 

Group 32

\(\left[ {\begin{array}{*{20}c} {1 \times 1} & {256} \\ {3 \times 3} & {256} \\ {1 \times 1} & {512} \\ \end{array} } \right] \times 4\)

 

Group 32

\(\left[ {\begin{array}{*{20}c} {1 \times 1} & {512} \\ {3 \times 3} & {512} \\ {1 \times 1} & {1024} \\ \end{array} } \right] \times 6\)

 

Group 32

Residual dilated convolution

\(B:\;\left[ {\begin{array}{*{20}c} {1 \times 1} & {1024} \\ {3 \times 3} & {256} \\ {1 \times 1} & {1024} \\ \end{array} } \right] \times 1\)

\(A:\;\left[ {\begin{array}{*{20}c} {1 \times 1} & {1024} \\ {3 \times 3} & {256} \\ {1 \times 1} & {1024} \\ \end{array} } \right] \times 2\)

Dilation 2

Stride 2

\(B:\;\left[ {\begin{array}{*{20}c} {1 \times 1} & {1024} \\ {3 \times 3} & {256} \\ {1 \times 1} & {1024} \\ \end{array} } \right] \times 1\)

\(A:\left[ {\begin{array}{*{20}c} {1 \times 1} & {1024} \\ {3 \times 3} & {256} \\ {1 \times 1} & {1024} \\ \end{array} } \right] \times 2\)

Dilation 2

Stride 2

fc-1024

fc-256

fc-2