Noise-Perception Multi-Frame Collaborative Network for Enhanced Polyp Detection in Endoscopic Videos

• DOI: 10.3390/electronics14010062
• Publication Date: 2024-12-27T07:59:27Z
• Authors: Haoran Li; Guoyong Zhen; Chengqun Chu; Yuting Ma; Yongnan Zhao
• Keywords: Feature (linguistics)

The accurate detection and localization of polyps during endoscopic examinations are critical for early disease diagnosis cancer prevention. However, the presence artifacts noise, along with high similarity between surrounding tissues in color, shape, texture complicates polyp video frames. To tackle these challenges, we deployed multivariate regression analysis to refine model introduced a Noise-Suppressing Perception Network (NSPNet) designed enhanced performance. NSPNet leverages wavelet transform enhance model’s resistance noise while improving multi-frame collaborative strategy dynamic videos, efficiently utilizing temporal information strengthen features across Specifically, High-Low Frequency Feature Fusion (HFLF) framework, which allows capture high-frequency details more effectively. Additionally, an improved STFT-LSTM Polyp Detection (SLPD) module that utilizes from sequences feature fusion environments. Lastly, integrated Image Augmentation (IAPD) improve performance on unseen data through preprocessing enhancement strategies. Extensive experiments demonstrate outperforms nine SOTA methods four datasets key metrics, including F1Score recall.