Table 3.

Specifications of the proposed and alternative approaches.

Model	Backbone	Params.	Upsampling	Target	Reference
DeepPyramid	VGG16	33.57 M	Bilinear	Medical Images	22
Adapt-Net	VGG16	24.69 M	Bilinear	Medical Images	17
UNet++	VGG16	24.24 M	Bilinear	Medical Images	33
ReCal-Net	VGG16	22.93 M	Bilinear	Medical Images	21
CPFNet	VGG16 | ResNet34	39.17 M | 34.66 M	Bilinear	Medical Images	34
CE-Net	VGG16 | ResNet34	33.50 M | 29.90 M	Trans Conv	Medical Images	35
FED-Net	ResNet50	59.52 M	Trans Conv & PixelShuffle	Liver Lesion	36
scSENet	VGG16 | ResNet34	22.90 M | 25.25 M	Bilinear	Medical Images	37
DeepLabV3+	ResNet50	26.68 M	Bilinear	Scene	38
UPerNet	ResNet50	51.26 M	Bilinear	Scene	39
U-Net+1	VGG16	22.55 M	Bilinear	Medical Images	40

In “Upsampling” column, “Trans Conv” stands for Transposed Convolution.

¹Note that UNet + is an improved version of UNet, where we use VGG16 as the backbone network and double convolutional blocks (two consecutive convolutions followed by batch normalization and ReLU layers) as decoder modules.