3D ARCNN: An Asymmetric Residual CNN for Decreasing False Positive Rate of Lung Nodules Detection

Abstract

Lung cancer is with the highest morbidity and mortality, and early detection of cancerous changes is essential to reduce the risk of death. To achieve this, it is necessary to reduce the false positive rate of detection. In this paper, we propose a novel asymmetric residual network, called 3D ARCNN, to reduce false positive rate of lung nodules detection. 3D ARCNN consists of asymmetric convolutional and multilayer cascaded residual network structures. To solve the problem of deep neural network with large amounts of parameters and poor reproduction ability, the proposed model uses asymmetric convolution to reduce model parameters and enhance the generalization ability of the model. In addition, the model uses an internally cascaded multi-stage residual to prevent the gradient vanishing and exploding problems of deep networks. Experiments are performed on the public dataset LUNA16. Our method achieved high detection sensitivity of 91.6%, 92.7%, 93.2% and 95.8% at 1, 2, 4 and 8 false positives per scan, respectively, which got an average CPM index of 0.912. Experimental results show that the proposed 3D ARCNN is very useful for reducing the false positive rate of lung nodules in the clinic.