病变
癌症
窄带成像
召回
分割
内窥镜检查
癌前病变
人工智能
胃
医学
放射科
阶段(地层学)
精确性和召回率
计算机科学
内科学
病理
心理学
生物
古生物学
认知心理学
作者
Kaili Qiu,Xiongzhu Bu,Hui Zhou,Leheng Liu
标识
DOI:10.1109/iwecai55315.2022.00099
摘要
Early gastric cancer develops through inflammation, intestinalization, low-grade neoplasia and other developmental lesions, yet most patients are already at an advanced stage of gastric cancer when detected. In order to effectively identify lesion sites from endoscopic images acquired by magnified endoscopy combined with narrow-band imaging (ME-NBI), this paper introduces deep learning into an intelligent assisted lesion diagnosis system for endoscopic images, using an improved UNet-based network for accurate lesion site segmentation. The experimental results show that the reconciliation parameter F-score of the accuracy and recall of the proposed model in this paper can reach 96%, indicating that the improved method has a better ability to identify early cancer and precancerous lesions in the stomach.
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