Abstract

Info

Author(s):: PAN Ziyan1; XING Suxia1; PANG Jianliang2; SHEN Nan1; WANG Yu1; LIU Zijiao1; JU Zihan1; 1. School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China 2. Air Force Medical Center, Beijing 100048, China

Keywords:: Keywords: lung nodule detection super-resolution convolutional neural network feature fusion bag-of-words model XGBoost

Abstract:: Abstract: A lung nodule detection model based on multi-feature fusion and XGBoost is proposed for improving the accuracy and efficiency of lung nodule detection. After candidate nodule regions are obtained by threshold segmentation and morphological operations, the features of candidate nodules are enhanced by super-resolution convolutional neural network. Then several methods including speeded up robust features, gray-level co-occurrence matrix and gray-level invariant moments are used to extract various local and global features of candidate nodules, and bag-of-words model is utilized for dimensionality reduction and fusion. Finally, XGBoost-decision tree classification model is used to remove false positive nodules and complete the detection of lung nodules. The experiments on LIDC-IDRI data show that the model can achieve an accuracy of 97.87% and a recall rate of 97.92%. The proposed model can be used to assist doctors in the diagnosis of lung nodules and has certain clinical application value.