
TOTAL VIEWS: 1282
Objective: To address the issue of insufficient integration and expression of multi-level features in pulmonary nodules by existing models, which affects the detection performance of small nodules, an improved pulmonary nodule detection algorithm based on YOLOv8 is proposed. Methods: In the backbone network of YOLOv8, the convolution downsampling operation with a stride of 2 is replaced with a Space-to-Depth downsampling operation, enabling more comprehensive extraction of subtle features to improve the detection of tiny nodules. At the end of the backbone network, a squeeze-and-excitation attention module and a global response normalization attention module are incorporated into the Spatial Pyramid Pooling Fast layer, with a fully connected layer established between them to enhance information interaction, thereby improving the model's ability to distinguish key features of pulmonary nodules. In the neck network, a Cross Stage Partial module is employed to integrate multi-level features extracted by the backbone network, achieving richer gradient combinations and reducing interference from redundant information. Results: The improved algorithm achieves precision, recall, and mean average precision of 96.1%, 96.3%, and 97.8%, respectively, on the LUNA16 dataset. Compared to the original YOLOv8 model, these metrics are improved by 3.4%, 2.0%, and 1.5%, respectively. Conclusion: The improved algorithm demonstrates enhancements across all evaluation metrics, effectively detecting small nodules while maintaining robust performance for larger pulmonary nodules.
Pulmonary nodule detection; Convolutional neural network; Small nodule detection; Attention; Feature fusion
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DFEY-Net: Deep Feature-Enhanced YOLOv8 for Pulmonary Nodule Detection
How to cite this paper: Ji Tian, Ping Yang. (2025) DFEY-Net: Deep Feature-Enhanced YOLOv8 for Pulmonary Nodule Detection. International Journal of Clinical and Experimental Medicine Research, 9(4), 460-473.
DOI: http://dx.doi.org/10.26855/ijcemr.2025.07.014