Journal of Guangxi Normal University(Natural Science Edition) ›› 2025, Vol. 43 ›› Issue (6): 92-106.doi: 10.16088/j.issn.1001-6600.2024123003

• Intelligence Information Processing • Previous Articles     Next Articles

Design of Lightweight Pulmonary Nodules Detection Network on CT Images with Dynamic Channel Pruning

YI Jianbing1,2*, HU Yayi1,2, CAO Feng1,2, LI Jun1,2, PENG Xin1,2, CHEN Xin1,2   

  1. 1. College of Information Engineering, Jiangxi University of Science and Technology, Ganzhou Jiangxi 341000, China;
    2. Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control (Jiangxi University of Science and Technology), Ganzhou Jiangxi 341000, China
  • Received:2024-12-30 Revised:2025-05-15 Published:2025-11-19

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Abstract: Lung cancer is the most deadly cancer in the world, and pulmonary nodules are the main manifestation of early-stage lung cancer. However, existing algorithms have problems with missed detections, false positives and high model complexity when detecting small lung tumors. To solve the above problems, a pulmonary nodules detection algorithm based on channel mask and dynamic channel pruning is proposed. Firstly, the algorithm uses cross-layer connection to fuse shallow features with deep features, and simplifies the path aggregation network, thereby obtaining more abundant features while reducing the number of model parameters. Secondly, the algorithm introduces the SE channel attention mechanism in the residual connection, and adaptively adjusting the weight of each channel to focus on the key information in the lesion in order to improve the detection ability of the algorithm for pulmonary lesions. Finally, the algorithm uses a channel mask for dynamic channel pruning, which completely retains the skip connections in the module, thereby enhancing the feature representation ability of the model. The proposed algorithm is compared with the YOLOv8n algorithm in the performance test on the LUNA16 dataset. The model weight of this algorithm is 0.3 MiB smaller than that of the YOLOv8n algorithm, and the recall rate and mAP@0.5 increase by 2.0 percentage points and 1.7 percentage points, respectively. The proposed algorithm is compared with the YOLOv8n algorithm in the performance test on the Lung-PET-CT-Dx dataset. The model weight of this algorithm is 0.9 MiB smaller than that of YOLOv8n algorithm, and the recall rate and mAP@0.5 increase by 0.8 percentage points and 0.4 percentage points, respectively. The experimental results show that the proposed model has the characteristics of higher accuracy and fewer parameters in lung cancer detection.

Key words: lung cancer detection, channel mask, dynamic channel pruning, cross-layer feature fusion, channel attention

CLC Number:  TP391.41
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