To solve the problems of low detection rate and high false positive of nodules in low-dose lung CT images by traditional computer-aided diagnosis system

a two-stage pulmonary nodules detection model based on U-Net network and attention mechanism was proposed. In order to improve the detection speed and detection rate of pulmonary nodules

a 3D network was constructed to detect the candidate nodules firstly. It optimized the detection speed while the three-dimensional information of nodules was fully utilized to improve the detection rate of the candidate nodules. Then

the multi-view input method was used to ensure that the spatial features of nodules was obtained. The sections from 9 angles in three-dimensional space

including sagittal plane

coronal plane and horizontal plane

were input into the network together.The ViT network was used as a feature extractor and combined with the feature pyramid network to achieve the classification of nodules

and we fused all section results to achieve the screening of false positive nodules. The final experimental results on LUNA16 data set show that the accuracy of the proposed model reaches 94.7%

which improves the accuracy and reduces the rate of misdiagnosis and missed diagnosis.