计算机工程2025，Vol.51Issue(6)：327-337,11.DOI:10.19678/j.issn.1000-3428.0070192
基于CNN-Transformer的电子喉镜病灶及器官分割网络
CNN-Transformer-Based Lesion and Organ Segmentation Network for Electronic Laryngoscope
摘要
Abstract
In electronic laryngoscopy,the variable morphology of lesions and organs,along with unclear boundaries between lesions,organs,and mucosal tissues,leads to unsatisfactory accuracy in image segmentation of lesions and major laryngeal organs.To address this problem,a CNN-Transformer two-stream hybrid network is proposed.The Convolutional Neural Network(CNN)branch extracts fine-grained features,whereas the Transformer branch extracts global semantic features.Specifically,the hybrid network first extracts fine-grained features at multiple scales in the image through the CNN branch and then fuses the extracted features with the global semantic features from the Transformer branch.This approach effectively captures both shallow,local fine-grained representations of features and deep,global information.A dark feature enhancement module is used to enhance the feature details in the darker regions of the image before performing multilevel feature fusion.To validate the effectiveness of the method,2 425 laryngoscopic surgical images from various medical institutions are used for experiments.The results are compared and analyzed with nine recently proposed methods,demonstrating the superiority of the proposed approach.关键词
电子喉镜/图像分割/双流混合网络/多尺度特征融合/暗部特征增强Key words
electronic laryngoscope/image segmentation/hybrid two-stream network/multi-level feature fusion/dark feature enhancement分类
信息技术与安全科学引用本文复制引用
李白芽..基于CNN-Transformer的电子喉镜病灶及器官分割网络[J].计算机工程,2025,51(6):327-337,11.基金项目
2022西安交通大学教改项目(22BJ07Z) （22BJ07Z）
陕西省2023年度"教师发展研究计划专项项目"(2023JSY027). （2023JSY027）
