EAGLE-Net: A hierarchical neural network for detecting anatomical landmarks in upper gastrointestinal endoscopy for clinical diagnosis

This study proposes a hierarchical network architecture, named EAGLE-Net, for identifying anatomical landmarks in the upper gastrointestinal (GI) tract endoscopic videos. Unlike conventional techniques, which label anatomical landmarks for static endoscopic images, the proposed method aims to classify landmarks from videos of the upper GI tract. Video streams often suffer from many noises and contaminated objects, which requires a new approach to tackle this issue. The proposed technique utilizes hierarchical network architecture, which consists of two stages: endoscopic image quality assessment and anatomical landmark classification. In the first stage, high-quality frames are preserved from GI tract videos. These frames are then used to identify a specific location among ten anatomical landmarks. The proposed method increases the coherence between the hierarchical data levels. It integrates an attention module to strengthen feature connections and utilizes a new hierarchical cross-entropy loss function to optimize model performance. The experimental results demonstrated that the proposed system achieves a high accuracy of 93% on average in both classification stages. In clinical experiments, anatomical landmarks are automatically denoted to help physicians monitor the endoscopy process. In addition, the proposed method demonstrates a potential solution for the deployment of a computer-aided diagnostic application for the detection and treatment of upper GI tract lesions.