GEEG-YOLOv8: Gaussian Enhanced Euclidean Norm Ghost Attention for Real-Time Polyp Detection

Abstract

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Research on computer-aided polyp detection in gastrointestinal endoscopy has spanned the past few decades. Despite notable progress, the challenge of achieving automatic accurate and real-time polyp detection remains unresolved. This is because of the large differences in polyp characteristics such as shape, texture, size, and color, and the artifacts that are similar to polyp during endoscopy procedure. In this paper, we propose a novel Gaussian Enhanced Euclidean norm Ghost attention (GEEG) module for reliable real-time polyp detection on endoscopic images and videos. The new attention mechanism strengthens the features generated by Ghost convolution’s cheap operations by increasing the ability to extract inter-channel and spatial information inside the convolution layer. This module is integrated into the backbone of YOLOv8, creating a new model named GEEG-YOLOv8, to overcome above obstacles in polyp detection. Experiment results on three public datasets show that our proposed method outperforms existing state-of-the-art methods in both accuracy and speed.