A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 7; pp. 2006 - 2015
• Main Authors: Cao, Haichao, Liu, Hong, Song, Enmin, Ma, Guangzhi, Xu, Xiangyang, Jin, Renchao, Liu, Tengying, Hung, Chih-Cheng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D-CNN; Architecture; Artificial neural networks; Clustering algorithms; Computed tomography; Computer-aided diagnosis; Convolutional neural networks; ensemble learning; Heterogeneity; Humans; Image detection; Image segmentation; Imaging, Three-Dimensional; Lung; Lung cancer; Lung Neoplasms - diagnostic imaging; lung nodule detection; Lung nodules; Medical diagnosis; Neural networks; Neural Networks, Computer; Nodules; Prediction methods; Radiographic Image Interpretation, Computer-Assisted - methods; Reduction; Survival; Tomography, X-Ray Computed - methods; Training; U-Net
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2019.2963720

Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, it is a challenge to develop a robust nodule detection method. In this study, we propose a two-stage convolutional neural networks (TSCNN) for lung nodule detection. The first stage based on the improved U-Net segmentation network is to establish an initial detection of lung nodules. During this stage, in order to obtain a high recall rate without introducing excessive false positive nodules, we propose a new sampling strategy for training. Simultaneously, a two-phase prediction method is also proposed in this stage. The second stage in the TSCNN architecture based on the proposed dual pooling structure is built into three 3D-CNN classification networks for false positive reduction. Since the network training requires a significant amount of training data, we designed a random mask as the data augmentation method in this study. Furthermore, we have improved the generalization ability of the false positive reduction model by means of ensemble learning. We verified the proposed architecture on the LUNA dataset in our experiments, which showed that the proposed TSCNN architecture did obtain competitive detection performance.