Abstract

Info

Title:: Classification of benign and malignant pulmonary nodules by deep learning with anatomy-based attention mechanism

Author(s):: LIU Yun; WANG Yida; ZHANG Chengxiu; YANG Guang; WANG Chenglong; Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China

Keywords:: Keywords: pulmonary nodule attention mechanism CT image deep learning

Abstract:: Abstract: Pulmonary nodule is the initial sign of lung cancer, and the timely detection and accurate diagnosis of malignant and benign nodules have great significance for the treatment of diseases. In order to improve the diagnostic accuracy of benign and malignant pulmonary nodules in pulmonary CT images, a novel convolution neural network based on 3D ResNet is proposed, with anatomy-based attention mechanism for greatly improving the classification accuracy of pulmonary nodules. In addition, the region of interest required for the attention mechanism is obtained by automatic segmentation, thereby achieving the full automation of the whole process. The addition of anatomy-based attention mechanism can better capture local texture information in CT images and further extract useful features for diagnosing benign and malignant pulmonary nodules. The proposed method is verified in LIDC-IDRI data set. The results show that the proposed method greatly improves classification accuracy as compared with other existed methods and traditional 3D ResNet, achieving an area under the receiver operating characteristic curve (AUC) of 0.973 and an accuracy of 94.0% in the independent test set. The proposed method has the potential to assist doctors in the diagnosis of pulmonary nodules.