%0 Journal Article %A YUAN Jinli %A ZHAO Linlin %A GUO Zhitao %A SU Yi %A LU Chenggang %T Improved U-Shaped Residual Network for Lung Nodule Detection %D 2022 %R 10.3778/j.issn.1002-8331.2011-0211 %J Computer Engineering and Applications %P 195-203 %V 58 %N 13 %X Aiming at the low sensitivity of lung nodule detection in computed tomography(CT) images, and there are a large number of false positives, an improved U-shaped residual network is proposed for lung nodule detection. Firstly, the U-shaped structure of U-net network and residual learning method are used to construct deep-seated network, At the same time, self-calibrated convolutions  is introduced to enhance the information extraction ability of features and enhance the inter channel and local information, which is conducive to the detection of different shapes of nodules. Secondly, through the introduction of channel attention mechanism, the features in the feature extraction process are recalibrated to realize adaptive learning feature weight. In addition, DR loss is introduced as the classification loss function of this algorithm to solve the imbalance problem of positive and negative samples. The proposed algorithm is validated in LUNA16 data set, and the CPM score reaches 0.901, which improves the sensitivity of pulmonary nodule detection, and effectively reduces the average number of false positive results, which can effectively assist radiologists to detect pulmonary nodules. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2011-0211