A Segmentation Algorithm of Colonoscopy Images Based on Multi-Scale Feature Fusion

Abstract

Colorectal cancer is a common malignant tumor. Colorectal cancer is primarily caused by the cancerization of an adenomatous polyp. Segmentation of polyps in computer-assisted enteroscopy images is helpful for doctors to diagnose and treat the disease accurately. In this study, a segmentation algorithm of colonoscopy images based on multi-scale feature fusion is proposed. The proposed algorithm adopts ResNet50 as the backbone network to extract features. The shallow features are processed using the cross extraction module, thus increasing the receptive field, retaining the texture information, and fusing the processed shallow features and deep features at different proportions based on a multi-proportion fusion module. The proposed algorithm is capable of suppressing redundant information, removing background noise, and sharpening boundaries while acquiring considerable semantic information. As revealed by the results of the experiments on the published Kvasir-SEG dataset of intestinal polyps, the mean Dice coefficient and mean intersection over union were obtained as 0.9192 and 0.8873, better than that of existing mainstream algorithms. The result verifies the effectiveness of the proposed network and provides a reference for deep learning concerning the image processing and analysis of intestinal polyps.