基于卷积神经网络的肺结节分类方法研究

为了提高肺结节自动恶性分类模型的性能，提出一种肺结节良恶性分类算法。首先，将3维肺结节CT图像作为模型输入；然后将双路径网络与卷积神经网络模型结合用于提取CT图像特征。其中，残差连接用于捕获更多高层和语义信息，密集连接用于降低模型的复杂度。在Luna16数据集上的实验结果表明，该算法的ROC可以达到90％，算法性能优于同类型算法性能。

A New Classification Method of Lung Nodules Based onConvolutional Neural Network

In view of an improvement of the performance of the automatic malignant classification model of lung nodules, a benign and malignant lung nodule classification algorithm has thus been proposed. First, 3D CT images of pulmonary nodules are used as a model input; then the CT image features are to be extracted with the dual-path network combined with VGG16, with the residual connection used to capture more high-level and semantic information, and the dense connection used to reduce the complexity of the model as well. The experimental results on the Luna16 dataset show that the ROC of the algorithm can reach as high as 90%, with its algorithm performance much better than the same type of algorithm.