Lung cancer has always threatening human health and life. As small pulmonary nodules are main early features of lung cancer, early screening for small pulmonary nodules through computed tomography (CT) imaging is essential for the treatment of lung cancer. In this paper, the YOLOv5 model is improved to improve the ability of detection and recognition of small pulmonary nodules in complex CT lung images. Firstly, the preprocessing step is put into effect to obtain the lung parenchyma in CT images. Then, the backbone structure of YOLOv5 is improved by iResNet to improve the ability of feature extraction, and the feature fusion network is improved by BiFPN to improve the detection ability of small pulmonary nodules. Finally, the strategy of group normalization is used to improve the model performance under small batch size training condition. The experimental results on LUNA16 data set show that the detection AP of the improved model reach 94.8%, the competitive index score is 0.895, and the sensitivity is 78.1%, 94.4%, under 1/8 and 1/4 FPs, respectively. Compared with other two-dimensional target detection models, the improved yolov5 model has better detection ability of small pulmonary nodules. And, the results are better than most other two-dimensional pulmonary nodule detection methods. In addition, compared with other three-dimensional pulmonary nodule detection methods.
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