Image Recognition and Diagnosis System of Early Gastric Cancer Based on Artificial Intelligence Algorithm

Early detection of gastric cancer dramatically improves patient outcomes, yet visual diagnosis during endoscopy remains operator-dependent and time-consuming. This paper presents an end-to-end Image Recognition and Diagnosis System for Early Gastric Cancer (IRDS-EGC) that combines deep convolutional neural networks for lesion detection, segmentation and classification on endoscopic images. We propose a hybrid architecture that ensembles a classification backbone (ResNet50 and EfficientNet-B3) with a U-Net-based segmentation head to localize suspicious regions and then aggregate features for final diagnosis. The system was trained and validated on a curated dataset of 10,000 annotated endoscopic images (3,500 early-gastric-cancer images, 6,500 benign) with clinically-informed augmentation and pre-processing. On a held-out test set (n = 1,500), the proposed system achieved a sensitivity of 94.3%, specificity of 90.3%, accuracy of 91.7%, and area under the ROC curve (AUC) of 0.955 for the binary task of early cancer vs non-cancer. Qualitative analysis shows the segmentation output closely aligns with clinician annotations. We discuss clinical integration pathways, limitations, and directions for future work including prospective validation and real-time endoscopic deployment.