Research on the Diagnosis Method of Pancreatic Lesions by Endoscopic Ultrasound Based on Twin Network Structure

Abstract

To tackle the endoscopic ultrasound (EUS) pancreatic visual information, we propose a novel twin diagnostic network architecture (TDN) which consists of two identical feature extraction network structures. Model 1 is used to distinguish the categories of pancreatic visual information. Model 2 distinguishes whether there is cancerous information in pancreatic visual information. If cancerous information is included, gradient-weighted class activation mapping (Grad-CAM) is employed to calculate the activation heat map of visual information to present the specific location of the cancerous area in the visual information. To effectively integrate detailed texture information with abstract semantic information, we find the optimal proportion relationship required for feature fusion in each stage output feature vector dimension. The experimental results show that the classification accuracy of the TDN network can reach 98.344% for the pancreatic part and 99.471% for the specific part of the pancreas whether canceration occurs.