Abstract

Info

Author(s):: CAI Tijian; JIANG Jiahao; LIU Zunxiong; ZHAO Shiming; YI Shengquan; School of Information and Software Engineering, East China Jiaotong University, Nanchang 330013, China

Keywords:: Keywords: colorectal cancer polyp segmentation deep learning dilated convolution context information

Abstract:: Abstract: A local context fusion based segmentation model which uses a local context attention mechanism to filter out irrelevant feature information and enhance the attention to important regions is presented for accurate polyp segmentation. The features at different scales are captured by multi-kernel dilated convolution for improving the accuracy of polyp boundary segmentation. Pyramid context selection module utilizes shallow encoder features to compensate for the low-level information lost by the deeper encoder, enabling the model to adapt to polyps of various sizes. The proposed model achieves accuracies of 97.67%, 97.19% and 99.23% on Kvasir-SEG, EndoScene and CVC-ClinicDB datasets, respectively, with mIoU of 91.20%, 88.31% and 94.75%, respectively, exhibiting higher accuracy and generalizability than the existing classical methods and validating its superior performance in polyp segmentation. The proposed model can improve polyp segmentation accuracy and provide a more accurate aid for polyp segmentation.