Deep learning for classification and localization of early gastric cancer in endoscopic images

•Based on the prepared dataset, a complete workflow for endoscopic images EGC-related classification and segmentation was introduced.•Image recognition was perfomed based on backbone networks. Simultaneously, combined with the pixel-level features of EGC, an effective guided attention module was designed to effectively extract EGC-realted feature regions.•Based on widely used U-Net, a lightweight attention module and multi-scale feature extractor were introduced to assist in automatic pathology localization of EGC and evaluate the semantic segmentation performance by comparing with other models.•A pilot study in endoscopic videos was conducted to verified the feasibility and practicality of the proposed computer-aided detection system.