基于深度卷积神经网络的肺结节检测算法

在传统的肺结节检测算法中,存在检测敏感度低,假阳性数量大的问题。针对这一问题,提出了基于深度卷积神经网络(CNN)的肺结节检测算法。首先,有目的性地简化传统的全卷积分割网络;然后,创新地加入对部分CNN层的深监督并使用改进的加权损失函数,获得高质量的候选肺结节,保证高敏感度;其次,设计了基于多尺度上下文信息的三维深度CNN来增强对图像的特征提取;最后,将训练得到的融合分类模型用于候选结节分类,以达到降低假阳率的目的。所提算法使用了LUNA16数据集,并通过对比实验验证算法的性能。在检测阶段,当每个CT检测出的候选结节数为50.2时,获得的敏感度为94.3%,与传统的全卷积分割网络相比提升了4.2个百分点;在分类阶段,竞争性能指标达到0.874。实验结果表明,所提算法能够有效提高检测敏感度和降低假阳率。

Pulmonary nodule detection algorithm based on deep convolutional neural network

In traditional pulmonary nodule detection algorithms, there are problems of low detection sensitivity and large number of false positives. To solve these problems, a pulmonary nodule detection algorithm based on deep Convolutional Neural Network (CNN) was proposed. Firstly, the traditional full convolution segmentation network was simplified on purpose. Then, in order to obtain high-quality candidate pulmonary nodules and ensure high sensitivity, the deep supervision of partial CNN layers was innovatively added and the improved weighted loss function was used. Thirdly, three-dimensional deep CNNs based on multi-scale contextual information were designed to enhance the feature extraction of images. Finally, the trained fusion classification model was used for candidate nodule classification to achieve the purpose of reducing false positive rate. The performance of algorithm was verified through comparison experiments on LUNA16 dataset. In the detection stage, when the number of candidate nodules detected by each CT (Computed Tomography) is 50.2, the sensitivity of this algorithm is 94.3%, which is 4.2 percentage points higher than that of traditional full convolution segmentation network. In the classification stage, the competition performance metric of this algorithm reaches 0.874. The experimental results show that the proposed algorithm can effectively improve the detection sensitivity and reduce the false positive rate.