Lung cancer is the leading cause of cancer death worldwide. Early detection of lung cancer is helpful to provide the best possible clinical treatment for patients. Due to the limited number of radiologist and the huge number of chest x-ray radiographs (CXR) available for observation, a computer-aided detection scheme should be developed to assist radiologists in decision-making. While deep learning showed state-of-the-art performance in several computer vision applications, it has not been used for lung nodule detection on CXR. In this paper, a deep learning-based lung nodule detection method was proposed. We employed patch-based multi-resolution convolutional networks to extract the features and employed four different fusion methods for classification. The proposed method shows much better performance and is much more robust than those previously reported researches. For publicly available Japanese Society of Radiological Technology (JSRT) database, more than 99% of lung nodules can be detected when the false positives per image (FPs/image) was 0.2. The FAUC and R-CPM of the proposed method were 0.982 and 0.987, respectively. The proposed approach has the potential of applications in clinical practice.

肺癌是全球癌症死亡的主要原因。早期发现肺癌有助于为患者提供最佳的临床治疗。由于放射科医生的数量有限，并且可供观察的胸部X光片（CXR）数量巨大，因此应开发一种计算机辅助检测方案，以协助放射科医生做出决策。虽然深度学习在多种计算机视觉应用中显示了最先进的性能，但尚未用于CXR的肺结节检测。本文提出了一种基于深度学习的肺结节检测方法。我们采用基于补丁的多分辨率卷积网络提取特征，并采用四种不同的融合方法进行分类。所提出的方法表现出更好的性能，并且比以前报道的研究更健壮。对于公开可用的日本放射技术学会（JSRT）数据库，当每幅图像的假阳性（FPs /图像）为0.2时，可以检测到超过99％的肺结节。该方法的FAUC和R-CPM分别为0.982和0.987。所提出的方法具有在临床实践中的应用潜力。