Abstract：Computed tomography (CT) screening for pulmonary nodules is an important method for early diagnosis of lung cancer. However, the automatic detection of pulmonary nodules, especially small nodules, is still challenging due to the large differences in shape, size and location of pulmonary nodules. To achieve highly sensitive detection of pulmonary nodules, in this paper, a new computer-aided detection system for pulmonary nodules detection was proposed. The system adopted two new strategies: size adaptive candidate test (SACD) and size adaptive false positive reduction (SAFPR). First, SACD combined deep and shallow convolution features to construct advanced features and detected CT images to obtain the location and size information of the region of interest. Then, the detection results were sent to three parallel sub-networks for screening different sizes of nodules, so as to refine the detection results of SACD and improved the accuracy and robustness of the computer-aided detection system. The results on the LIDC-IDRI dataset (1186 nodules) demonstrated that the proposed system achieved the high sensitivity of 96% at 1 FPs/scan, which was superior or comparable to the state-of-the-art systems, while in an independent dataset containing 430 nodules, the detection sensitivity of the system was 69.53% at 0.3 FPs/ scan for the nodules with the size of 3.89±2.34 mm, which was comparable to the human screening results of two experienced radiologists, indicating that the system has certain clinical application value.
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