〔Abstract〕 Objective To develop an endoscopic automatic detection system in early gastric cancer(EGC) based on a region-based convolutional neural network(Mask R-CNN). Methods A total of 3 579 and 892 white light images(WLI) of EGC were obtained from the First Affiliated Hospital of Anhui Medical University for training and testing, respectively. Then, 10 WLI videos were obtained prospectively to test dynamic performance of the RCNN system. In addition, 400 WLI images were randomly selected for comparison with the Mask R-CNN system and endoscopists. Diagnostic ability was assessed by accuracy, sensitivity, specificity, positive predictive value(PPV), and negative predictive value(NPV). Results The accuracy, sensitivity and specificity of the Mask R-CNN system in diagnosing EGC in WLI images were 90.25%, 91.06% and 89.01%, respectively, and there was no significant statistical difference with the results of pathological diagnosis. Among WLI real-time videos, the diagnostic accuracy was 90.27%. The speed of test videos was up to 35 frames/s in real time. In the controlled experiment, the sensitivity of Maks R-CNN system was higher than that of the experts(93.00%vs80.20%,χ2=7.059,P<0.001), and the specificity was higher than that of the juniors(82.67%vs71.87%, χ2=9.955,P<0.001), and the overall accuracy rate was higher than that of the seniors(85.25%vs78.00%, χ2=7.009,P<0.001). Conclusion The Mask R-CNN system has excellent performance for detection of EGC under WLI, which has great potential for practical clinical application.