In this paper, we propose a novel and efficient false positive reduction framework for early pulmonary nodules detection with physical examination Computer Tomography (CT) scan. First, a 3DU-NET model is used for detecting small pulmonary nodules in physical examination. Second, the grayscale history image (GHI) of the candidate nodules in multi-view is used as the input of the false positive reduction network to obtain their comprehensive information. Finally, an effective multi-branch false positive reduction network is built to avoid reducing the sensitivity of nodule detection. Experiments are performed in our dataset, i.e. 1,000 samples of Lung Nodules from Physical Examination (LNPE1000), in which nodules are with the average diameter of 5. 3 mm. The sensitivities of the proposed method are 79.9% and 85.8% with each CT contained 0.25 and 0.45 false positives on average respectively, which are more than 8.9% and 3.9% higher than baseline. Experimental results show that the proposed method is robust and efficient, which can successfully detect smaller and larger nodules in physical examination and can effectively reduce false positives while maintain the sensitivity of nodule detection. It may be helpful to further improve the efficiency of doctors in clinical diagnosis.