Reducing FPs in Nodule Detection Using Neural Networks Ensemble

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 scheme1 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.