CMNet: deep learning model for colon polyp segmentation based on dual-branch structure

Xuguang Cao; Kefeng Fan; Cun Xu; Huilin Ma; Kaijie Jiao

doi:10.1117/1.JMI.11.2.024004

23 March 2024 CMNet: deep learning model for colon polyp segmentation based on dual-branch structure

Xuguang Cao, Kefeng Fan, Cun Xu, Huilin Ma, Kaijie Jiao

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 2, 024004 (March 2024). https://doi.org/10.1117/1.JMI.11.2.024004

Abstract

Purpose

Colon cancer is one of the top three diseases in gastrointestinal cancers, and colon polyps are an important trigger of colon cancer. Early diagnosis and removal of colon polyps can avoid the incidence of colon cancer. Currently, colon polyp removal surgery is mainly based on artificial-intelligence (AI) colonoscopy, supplemented by deep-learning technology to help doctors remove colon polyps. With the development of deep learning, the use of advanced AI technology to assist in medical diagnosis has become mainstream and can maximize the doctor’s diagnostic time and help doctors to better formulate medical plans.

Approach

We propose a deep-learning model for segmenting colon polyps. The model adopts a dual-branch structure, combines a convolutional neural network (CNN) with a transformer, and replaces ordinary convolution with deeply separable convolution based on ResNet; a stripe pooling module is introduced to obtain more effective information. The aggregated attention module (AAM) is proposed for high-dimensional semantic information, which effectively combines two different structures for the high-dimensional information fusion problem. Deep supervision and multi-scale training are added in the model training process to enhance the learning effect and generalization performance of the model.

Results

The experimental results show that the proposed dual-branch structure is significantly better than the single-branch structure, and the model using the AAM has a significant performance improvement over the model not using the AAM. Our model leads 1.1% and 1.5% in mIoU and mDice, respectively, when compared with state-of-the-art models in a fivefold cross-validation on the Kvasir-SEG dataset.

Conclusions

We propose and validate a deep learning model for segmenting colon polyps, using a dual-branch network structure. Our results demonstrate the feasibility of complementing traditional CNNs and transformer with each other. And we verified the feasibility of fusing different structures on high-dimensional semantics and successfully retained the high-dimensional information of different structures effectively.

Citation Download Citation

Xuguang Cao, Kefeng Fan, Cun Xu, Huilin Ma, and Kaijie Jiao "CMNet: deep learning model for colon polyp segmentation based on dual-branch structure," Journal of Medical Imaging 11(2), 024004 (23 March 2024). https://doi.org/10.1117/1.JMI.11.2.024004

Received: 3 August 2023; Accepted: 4 March 2024; Published: 23 March 2024

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KEYWORDS

Polyps

Colon

Image segmentation

Education and training

Data modeling

Performance modeling

Deep learning

Purpose

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