Abstract

Info

Author(s):: TIAN Ji; YANG Ping; LIU Jia; WANG Jinhua; College of Smart City, Beijing Union University, Beijing 100101, China

Keywords:: Keywords: YOLOv5 lung nodule detection downsampling algorithm attention mechanism hard sample

Abstract:: To address the challenges of detecting small nodules in lung CT images and achieving a balance between lightweight and high-precision with the existing lung nodule detection algorithms, a high-precision and lightweight lung nodule detection algorithm based on improved YOLOv5 is proposed. The main improvements are focused on 4 aspects. (1) Replacing the stride-2 downsampling operation in the YOLOv5 backbone with space-to-depth downsampling operations to enhance fine feature extraction for detecting small nodules more comprehensively. (2) Employing an asymptotic feature pyramid network in the YOLOv5 neck to establish connections among feature maps from different paths, thereby enhancing interaction among different hierarchical levels. (3) Introducing global context-aware attention to the end of YOLOv5 neck network for improving the models ability to understand key features and semantic information of lung nodules from a global perspective. (4) Utilizing the loss rank mining approach to strategically train on hard samples, thereby strengthening the models discrimination ability. The improved algorithm achieves 96.0% precision, 95.0% recall rate and 97.3% average precision on the LUNA16 dataset, which are 14.0%, 10.2% and 12.1% higher than the original YOLOv5 model, demonstrating its effectiveness for lung nodule detection.