Abstract
Colonoscopy is currently one of the main methods for the detection of rectal polyps, rectal cancer, and other diseases. With the rapid development of computer vision, deep learning–based semantic segmentation methods can be applied to the detection of medical lesions. However, it is challenging for current methods to detect polyps with high accuracy and real-time performance. To solve this problem, we propose a multi-branch feature fusion network (MBFFNet), which is an accurate real-time segmentation method for detecting colonoscopy. First, we use UNet as the basis of our model architecture and adopt stepwise sampling with channel multiplication to integrate features, which decreases the number of flops caused by stacking channels in UNet. Second, to improve model accuracy, we extract features from multiple layers and resize feature maps to the same size in different ways, such as up-sampling and pooling, to supplement information lost in multiplication-based up-sampling. Based on mIOU and Dice loss with cross entropy (CE), we conduct experiments in both CPU and GPU environments to verify the effectiveness of our model. The experimental results show that our proposed MBFFNet is superior to the selected baselines in terms of accuracy, model size, and flops. mIOU, F score, and Dice loss with CE reached 0.8952, 0.9450, and 0.1602, respectively, which were better than those of UNet, UNet++, and other networks. Compared with UNet, the flop count decreased by 73.2%, and the number of participants also decreased. The actual segmentation effect of MBFFNet is only lower than that of PraNet, the number of parameters is 78.27% of that of PraNet, and the flop count is 0.23% that of PraNet. In addition, experiments on other types of medical tasks show that MBFFNet has good potential for general application in medical image segmentation.
Highlights
Medical image processing is an important part of medical processes
Image segmentation is very important for the detection of lesions and organs, which significantly aids the development of medical automation, reduces the burden on medical workers, and reduces the incidence of medical accidents caused by human error (Litjens et al, 2017)
The semantic segmentation method of artificial intelligence can be used to assist colonoscopy detection, which can significantly reduce the risk of misjudgment and the omission of medical workers for various reasons, resulting in polyp canceration, colorectal tumor lesions, and colorectal cancer from early to late stages, as well as delayed treatment (Akbari et al, 2018)
Summary
Medical image processing is an important part of medical processes. At present, the main research directions in medical image processing include image segmentation, structure analysis, and image recognition. In medical automation and to achieve the early prevention of colorectal cancer, it is important to propose a method that segments polyps with sufficient accuracy to prevent the missed detection of polyps and to ensure that the model will not be too bloated, leading to slow speed. (2) Avoid superimposing feature information on channel dimensions only through feature maps to retain feature information, which will lead to an overbloated feature map in the last few layers of the feature map, resulting in the model requiring a large number of calculations Based on these strategies, we propose a multi-branching feature fusion network for polyp segmentation. The detailed model structure and parameter number verification are described in section “Materials and Methods,” the experimental part of the model is discussed in section “Experiments,” and a summary of the model is presented in section “Conclusion.”
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
