Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of
Citation: LI Yiyang, ZHAO Jiayi, YU Ruoyi, LIU Huixiang, LIANG Shuang, GU Yu. Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network. Journal of Biomedical Engineering, 2024, 41(5): 911-918. doi: 10.7507/1001-5515.202312014 Copy
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