# DuDeM: A Dual-Network Model for Early Gastric Cancer Detection Based on Capsule Endoscopy

## TL;DR

DuDeM is a new AI model that improves early detection of gastric cancer using capsule endoscopy images.

## Contribution

The novel dual-network model combines ResNet50 and CapsuleNet with dynamic routing for robust gastric cancer detection.

## Key findings

- DuDeM achieved an AUC of 0.981 and an F1-score of 0.979 for gastric cancer detection.
- The model maintained over 97% sensitivity, specificity, and precision with minimal performance degradation under image perturbations.

## Abstract

Early detection is critical for improving outcomes in gastric cancer, yet lesion recognition in capsule endoscopy is challenged by interference from different gastric anatomical sites, patient posture changes, and gastric peristalsis. This study aims to prompt a robust deep learning model to address these challenges. A dual-network model, named DuDeM (DualNet Detection Model), was developed by integrating a ResNet50-based convolutional branch with a CapsuleNet branch incorporating dynamic routing. The convolutional branch extracts local lesion features that are transmitted to primary capsules, while dynamic routing enables adaptive matching between capsule layers to establish local–global feature associations. An attention-weighted strategy is applied for feature fusion. The model was trained using capsule endoscopy images from nine hospitals in China and public datasets, and its performance was compared with eight representative models, with ablation analyses validating key components. Results showed that DuDeM achieved an area under the curve (AUC) of 0.981 and an F1-score of 0.979, with sensitivity, specificity, and precision all exceeding 97%, and performance degradation limited to within 3% under mild image perturbations. These findings suggest that DuDeM enables reliable early gastric cancer (EGC) recognition and may support large-scale capsule endoscopy screening in clinical practice.