Reducing FPs in Nodule Detection Using Neural Networks Ensemble

摘要：

In this paper, we employed neural network ensemble for FPs reduction in detecting lung nodules in chest radiographs. In our scheme, the ensemble consists of four modified forward neural networks, each one of them was trained with the back propagation algorithM to distinct a different type of non-nodules from nodules. The outputs of all the individual neural networks were combined by a modified forward mixing ANN. The performance of our scheme for false positive reduction was evaluated by use of FROC. With neural network ensemble, the false positive rate of CAd schemel was reduced for 44% (from 2.86 to 1.6 positives per image), at an overall sensitivity of 60%. We also compared our scheme with other researches. The result demonstrates the superiority of it over other ones. We believe that the proposed method is useful in false positives reduction in the diagnosis of lung nodules in chest radiograph.

关键词： False Positive Reduction Computer Aided Diagnosis (CAD) Radiograph Neural network

作者: Zhenghao Shi Kenji Suzuki Lifeng He

作者单位: School of Computer Science and Engineering, Xian University of Technology, Xian, 710048, China Department of Radiology, the University of Chicago, USA Aichi Prefectural University, Nagakute-cho, Aichi, 480-1198, Japan

会议类型: 国际会议

会议名称: Second International Symposium on Information Science and Engineering(第二届信息科学与工程国际会议)

会议地点: 上海

会议语种:英文

页码: 331-333

在线出版日期: 2009-12-26（万方平台首次上网日期，不代表论文的发表时间）