[Retracted] Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks

Table 1

Basic parameters of DenseNet.

Layers	DenseNet ()	Output size
Input	/	256256
Convolution	77 conv, stride 2	128128
Pooling	33 max pool, stride 2	6464
Dense block 1		6464
Transition layer 1	11 conv; 22 average pool, stride 2	6464; 3232
Dense block 2		3232
Transition layer 2	11 conv; 22 average pool, stride 2	3232; 1616
Dense block 3		1616
Transition layer 3	11 conv; 22 average pool, stride 2	1616; 88
Dense block 4		88