The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result is heavily dependent on the performance of each step in lung nodule detection, causing low classification accuracy and high false positive rate. In order to alleviate these issues, a lung nodule classification method based on a deep residual network is proposed. Abandoning traditional image processing methods and taking the 50-layer ResNet network structure as the initial model, the deep residual network is constructed by combining residual learning and migration learning. The proposed approach is verified by conducting experiments on the lung computed tomography (CT) images from the publicly available LIDC-IDRI database. An average accuracy of 98.23% and a false positive rate of 1.65% are obtained based on the ten-fold cross-validation method. Compared with the conventional support vector machine (SVM)-based CAD system, the accuracy of our method improved by 9.96% and the false positive rate decreased by 6.95%, while the accuracy improved by 1.75% and 2.42%, respectively, and the false positive rate decreased by 2.07% and 2.22%, respectively, in contrast to the VGG19 model and InceptionV3 convolutional neural networks. The experimental results demonstrate the effectiveness of our proposed method in lung nodule classification for CT images.

中文翻译：

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基于深度残差网络和迁移学习的肺结节分类。

传统的计算机辅助检测（CAD）系统中肺结节检测的分类过程复杂，分类结果在很大程度上取决于肺结节检测各步骤的性能，导致分类准确性低，假阳性率高。为了缓解这些问题，提出了一种基于深度残差网络的肺结节分类方法。摒弃传统的图像处理方法，以50层ResNet网络结构为初始模型，将残差学习和迁移学习相结合，构建了深度残差网络。通过对来自公开可用的LIDC-IDRI数据库的肺部计算机断层扫描（CT）图像进行实验，验证了所提出的方法。平均准确度为98.23％，假阳性率为1。基于十倍交叉验证方法，可得到65％。与传统的基于支持向量机（SVM）的CAD系统相比，我们的方法的准确性提高了9.96％，误报率降低了6.95％，而准确性分别提高了1.75％和2.42％，与VGG19模型和InceptionV3卷积神经网络相比，阳性率分别降低了2.07％和2.22％。实验结果证明了我们提出的方法在肺结节CT图像分类中的有效性。与VGG19模型和InceptionV3卷积神经网络相比，假阳性率分别降低了2.07％和2.22％。实验结果证明了我们提出的方法在肺结节CT图像分类中的有效性。与VGG19模型和InceptionV3卷积神经网络相比，假阳性率分别降低了2.07％和2.22％。实验结果证明了我们提出的方法在肺结节CT图像分类中的有效性。