基于NRC和多模态残差神经网络的肺部肿瘤良恶性分类

针对深度卷积神经网络训练时的网络退化、特征表达能力不强等问题,提出一种基于非负表示分类和多模态残差神经网络的肺部肿瘤(residual neural network-non negative representation classification, resnet-NRC)良恶性分类方法。使用迁移学习将预训练残差神经网络模型初始化参数;分别用CT、PET和PET/CT 3个模态的数据集训练残差神经网络,提取全连接层的特征向量;采用非负表示分类器(non-negative representation classification, NRC)对特征向量进行非负表示,求解非负系数矩阵;利用残差相似度进行肺部肿瘤良恶性分类。通过AlexNet、GoogleNet、ResNet-18/50/101模型进行对比试验,试验结果表明,ResNet-NRC分类效果优于其它模型,且特异性和灵敏度等各项评价指标也较高,该方法具有较好的鲁棒性和泛化能力。

Lung tumor benign-malignant classification based on multi-modal residual neural network and NRC algorithm

A method for the benign and malignant classification of lung tumors was put forward due to Challenges with the training of deep convolutional neural networks, network degradation and a weak ability to express the features based on non-negative representation classification and a multi-modal residual neural network. The pre-trained residual neural network model was initialized using transfer learning. three data sets(CT, PET and PET/CT)were used to train the network and extract the feature vectors of the fully connected layer, then a non-negative representation classifier was used for the non-negative representation of the feature vector, and used to solve the non-negative coefficient matrix. The residual similarity was used to classify benign and malignant lung tumors. Comparative experiments were conducted with the AlexNet, GoogleNet and ResNet-18/50/101 models. The experimental results showed that the classification accuracy of the ResNet-NRC was better than the other models, and the specificity and sensitivity indices were also higher. The proposed method has improved robustness and generalization ability.