  Medical image segmentation is the technique that helps doctor view and has a
precise diagnosis, particularly in Colorectal Cancer. Specifically, with the
increase in cases, the diagnosis and identification need to be faster and more
accurate for many patients; in endoscopic images, the segmentation task has
been vital to helping the doctor identify the position of the polyps or the
ache in the system correctly. As a result, many efforts have been made to apply
deep learning to automate polyp segmentation, mostly to ameliorate the U-shape
structure. However, the simple skip connection scheme in UNet leads to
deficient context information and the semantic gap between feature maps from
the encoder and decoder. To deal with this problem, we propose a novel
framework composed of ConvNeXt backbone and Multi Kernel Positional Embedding
block. Thanks to the suggested module, our method can attain better accuracy
and generalization in the polyps segmentation task. Extensive experiments show
that our model achieves the Dice coefficient of 0.8818 and the IOU score of
0.8163 on the Kvasir-SEG dataset. Furthermore, on various datasets, we make
competitive achievement results with other previous state-of-the-art methods.

Image and Video Processing;Computer Vision and Pattern Recognition