【摘要】 结合白光和窄带成像（NBI）两种光源的胃镜图像，阐述基于深度学习识别算法的早期胃癌智能识别系统的设计，利用采集、制作的10 942张胃部早癌医疗图像训练深度学习模型。针对收集到的大量早期胃癌图像的数据特点，对算法模型进行了置信度阈值调优，提升模型在多指标下的综合效果。结合医师在实际使用时的实时需求，提出一种视频时序投票方法对视频预处理及后处理策略进行优化。更多还原

【Abstract】 Combined with the gastroscopic images of white light and narrowband imaging(NBI),this paper describes the design of an intelligent recognition system for early gastric cancer based on depth learning recognition algorithm, and uses 10942 medical images of early gastric cancer collected and produced to train the depth learning model. According to the data characteristics of a large number of early gastric cancer images collected, the confidence threshold of the algorithm model is optimized to improve the comprehensive effect of the model under multiple indicators. According to the real-time requirements of doctors in actual use, a video timing voting method is proposed to optimize the video pre-processing and post-processing strategies.更多还原