LN-DETR: cross-scale feature fusion and re-weighting for lung nodule detection - Dibin Zhou; Honggang Xu; Wenhao Liu; Fuchang Liu (Scientific Reports)
Abstract With advancements in technology, lung nodule detection has significantly improved in both speed and accuracy. However, challenges remain in deploying these methods in complex real-world scenarios. This paper introduces an enhanced lung nodule detection algorithm base on RT-DETR, called LN-DETR. First, we designed a Deep and Shallow Detail Fusion layer that effectively fuses cross-scale features from both shallow and deep layers. Second, we optimized the computational load of the backbone network, effectively reducing the overall scale of the model. Finally, an efficient downsampling is designed to enhance the detection of lung nodules by re-weighting contextual information. Experiments conducted on the public LUNA16 dataset demonstrate that the proposed method, with a reduced number of parameters and computational overhead, achieves 83.7% mAP@0.5 and 36.3% mAP@0.5:0.95, outperforming RT-DETR in both model size and accuracy. These results highlight the superior detection accuracy of the proposed network while maintaining computational efficiency.