3D ARCNN: An Asymmetric Residual CNN for False Positive Reduction in Pulmonary Nodule

Abstract

Lung cancer is with the highest morbidity and mortality, and detecting cancerous lesions early is essential for reducing mortality rates. Deep learning-based lung nodule detection techniques have shown better scalability than traditional methods. However, pulmonary nodule test results often include a number of false positive outcomes. In this paper, we present a novel asymmetric residual network called 3D ARCNN that leverages 3D features and spatial information of lung nodules to improve classification performance. The proposed framework uses an internally cascaded multi-level residual model for fine-grained learning of lung nodule features and multi-layer asymmetric convolution to address the problem of large neural network parameters and poor reproducibility. We evaluate the proposed framework on the LUNA16 dataset and achieve a high detection sensitivity of 91.6%, 92.7%, 93.2%, and 95.8% for 1, 2, 4, and 8 false positives per scan, respectively, with an average CPM index of 0.912. Quantitative and qualitative evaluations demonstrate the superior performance of our framework compared to existing methods. 3D ARCNN framework can effectively reduce the possibility of false positive lung nodules in the clinical.