SPCF-YOLO: An Efficient Feature Optimization Model for Real-Time Lung Nodule Detection.

Abstract

Accurate pulmonary nodule detection in CT imaging remains challenging due to fragmented feature integration in conventional deep learning models. This paper proposes SPCF-YOLO, a real-time detection framework that synergizes hierarchical feature fusion with anatomical context modeling. First, the space-to-depth convolution (SPDConv) module preserves fine-grained features in low-resolution images through spatial dimension reorganization. Second, the shared feature pyramid convolution (SFPConv) module is designed to dynamically extract multi-scale contextual information using multi-dilation-rate convolutional layers. Incorporating a small object detection layer aims to improve sensitivity to small nodules. This is achieved in combination with the improved pyramid squeeze attention (PSA) module and the improved contextual transformer (CoTB) module, which enhance global channel dependencies and reduce feature loss. The model achieves 82.8% mean average precision (mAP) and 82.9% F1 score on LUNA16 at 151 frames per second (representing improvements of 17.5% and 82.9% over YOLOv8 respectively), demonstrating real-time clinical viability. Cross-modality validation on SIIM-COVID-19 shows 1.5% improvement, confirming robust generalization.