  Biomedical image segmentation is a very important part in disease diagnosis.
The term "colonic polyps" refers to polypoid lesions that occur on the surface
of the colonic mucosa within the intestinal lumen. In clinical practice, early
detection of polyps is conducted through colonoscopy examinations and
biomedical image processing. Therefore, the accurate polyp image segmentation
is of great significance in colonoscopy examinations. Convolutional Neural
Network (CNN) is a common automatic segmentation method, but its main
disadvantage is the long training time. Transformer utilizes a self-attention
mechanism, which essentially assigns different importance weights to each piece
of information, thus achieving high computational efficiency during
segmentation. However, a potential drawback is the risk of information loss. In
the study reported in this paper, based on the well-known hybridization
principle, we proposed a method to combine CNN and Transformer to retain the
strengths of both, and we applied this method to build a system called MugenNet
for colonic polyp image segmentation. We conducted a comprehensive experiment
to compare MugenNet with other CNN models on five publicly available datasets.
The ablation experiment on MugentNet was conducted as well. The experimental
results show that MugenNet achieves significantly higher processing speed and
accuracy compared with CNN alone. The generalized implication with our work is
a method to optimally combine two complimentary methods of machine learning.
