A Lightweight Polyp Image Segmentation Model Using Deep Convolution Kernel Modules and Nonlinear Units in Colonoscopy

Polyp image segmentation, as one of the important components of medical image processing tasks, provides a powerful auxiliary role for clinical colonoscopy. However, it is very difficult to implement lightweight and high-performance image segmentation models on general platforms, which poses a huge challenge to autonomous AI-driven colonoscopy. In this paper, we propose a lightweight Polyp Image Segmentation Model, including two key components. Firstly, this paper proposes a lightweight deep convolution kernel module (DCKM) to balance segmentation accuracy and inference time. The DCKM with a residual structure connects the input and output so that the output is corrected by the input, thereby improving the segmentation accuracy. Secondly, the proposed DCKM in this paper directly uses a multi-scale convolution structure instead of a complex residual structure to extract local features, reducing the inference time of the proposed model and realizing a lightweight image segmentation model. Finally, we introduce a nonlinear unit (NU) by connecting the convolution block with a nonlinear activation function to realize a nonlinear codec structure to balance the accuracy loss of the model segmentation results caused by the lightweight DCKM. We evaluate the inference time and segmentation performance of the proposed model on four public polyp datasets. Experimental results show that the proposed model reduces the model inference time and obtains better segmentation performance compared with the state-of-the-art methods.