Patent ID: 11880974
Assignee: ZHUHAI HENGQIN SANMED AITECH INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G  C | IPC G

Claim 1:
2. The method according to claim 1, wherein in sequentially performing the convolution processes of the first convolution layer, the second convolution layer, the third convolution layer, and the fourth convolution layer on the cell nucleus sample image to acquire the first feature image:
a size of the cell nucleus sample image is 320*320, a number of convolution kernels of the first convolution layer is 32, a size of the convolution kernels is 3*3 and a step size is 1, and a size of the output feature image is 320*320;
the second convolution layer comprises a first convolution sublayer and a second convolution sublayer, wherein a number of convolution kernels of the first convolution sublayer is 32, a size of the convolution kernels is 3*3 and a step size is 2, and a size of the output feature image is 160*160;
and a number of convolution kernels of the second convolution sublayer is 64, a size of the convolution kernels is 1*1 and a step size is 1, and a size of the output feature image is 160*160;
the third convolution layer comprises a third convolution sublayer and a fourth convolution sublayer, wherein a number of convolution kernels of the third convolution sublayer is 64, a size of the convolution kernels is 3*3 and a step size is 1, and a size of the output feature image is 160*160; and a number of convolution kernels of the fourth convolution sublayer is 128, a size of the convolution kernels is 1*1 and a step size is 2, and a size of the output feature image is 80*80; and
the fourth convolution layer comprises a fifth convolution sublayer and a sixth convolution sublayer, wherein a number of convolution kernels of the fifth convolution sublayer is 128, a size of the convolution kernel is 3*3 and a step size is 1, and a size of the output feature image is 80*80; and a number of convolution kernels of the sixth convolution sublayer is 128, a size of the convolution kernels is 1*1 and a step size is 1, and a size of the output feature image is 80*80, the feature image output by the sixth convolution sublayer being the first feature image.