ing at the problems that traditional lung nodules detection methods can only get low sensitivities and a lot of false positives, this paper presents a retrieval method for lung nodules CT image based on end-to-end two-dimensional full convolution object recognition network (2D FCN) and three-dimensional target classification convolution neural network (3D CNN). Firstly, the method builds the 2D CNN for candidate selection to detect and locate the suspected regions on axial slices, and outputs an image that is the same size as the original image and is marked. Secondly, the three-dimensional patches of each candidate are extracted to train the 3D CNN. Finally, the trained 3D model is used to classify the false positive nodules. Experimental results on the LIDC-IDRI dataset show that the proposed method can achieve the recall rate of nodules of 98.2% at 36.2 false positives per scan. In the false positive reduction, the method respectively achieves high detection sensitivities of 87.3% and 97.0% at 1 and 4 false positives per scan. Experimental results on the LIDC-IDRI dataset show that the proposed method is highly suited to be used for lung nodules detection, achieves high recall rate and accuracy and outperforms the current reported method. Meanwhile, the proposed framework is general and can be easily extended to many other 3D object detection tasks from volumetric medical images, and it has an important application value in clinical practice with the aid of radiologists and surgeons.