融合卷积神经网络和流形学习的肺结节检测

U-net是常用的医学图像分割网络,但仍存在卷积神经网络中泛化能力差、容易过拟合的缺点.针对其缺点,研究全卷积肺结节分割网络,引入随机失活层,采用新的激活函、损失函数、优化器等改进网络结构,改进后的网络具有更高的查全率.然后融合改进重构权值的局部线性嵌入算法对特征进行提取,最后采用XGBoost分类器进行最后的筛选分类.通过实验验证表明,得到实融合以上两种算法的肺结节检测具有更高的准确率更高的准确率和更好的泛化性,可以应用于肺结节检测.

Lung nodule detection using convolutional neural network and manifold learning

U-net is a commonly used medical image segmentation network, but it still has the disadvantages of poor generalization ability and easy over-fitting in convolutional neural networks. In view of its shortcomings, a full convolution lung nodule segmentation network is studied, adding dropout layers , using new activation function, loss function, optimizer, etc, to improve the network structure. The improved network has a higher recall rate. Then, the local linear embedding algorithm with improved reconstruction weights is used to extract features, and finally the XGBoost classifier is used for final classification. Through experimental comparison, it is confirmed that the pulmonary nodule detection combined with the above two algorithms has a higher accuracy rate.