基于改进深度卷积对抗生成网络的肺结节良恶性分类

Classification of Benign and Malignant Lung Nodules Based on Improved Deep Convolutional Generative Adversarial Network

为提高肺结节良恶性识别的准确率,构建改进深度卷积对抗生成网络(DCGAN)框架与半监督模糊C均值(FCM)聚类结合的SFDG肺结节良恶性识别模型。将带有良恶性等级标签的肺结节图像输入到DCGAN框架,使得只有来源分类能力的判别器网络同时具备肺结节等级分类能力。在判别过程中运用半监督FCM聚类方法,对输入肺结节图像进行特征提取和量化,将输出的当前图像所属类别概率及判别结果与真实结果进行比较来调整网络参数。通过设定加权损失函数最大概率提高模型识别准确率,训练得出具有良好鲁棒性的网络模型。实验结果表明,改进模型的判别器网络具有良好的肺结节良恶性分类能力,准确率高达90.96%。

In order to improve the accuracy of benign and malignant identification of pulmonary nodules,this paper proposes an SFDG model for benign and malignant identification of pulmonary nodules with improved Deep Convolutional Generative Adversarial Network(DCGAN)framework and semi-supervised Fuzzy C Means(FCM)clustering.Firstly,input lung nodule images with benign and malignant grade labels are input into the DCGAN framework,which enables the discriminator network with only the source classification ability to classify lung nodules.Then,the semi-supervised FCM clustering method is added into the discriminating process,performing clustering analysis on the raw data set after the model extracts and quantifies the features of input lung nodule images.The network parameters are adjusted by comparing the output category probability and discriminant result of the current image with the actual result.Finally,the recognition accuracy of the model is improved by setting the maximum probability of weighted loss function.Through training a network model with strong ability to identify benign and malignant pulmonary nodules is obtained.The experimental results show that the discriminator network of the improved model has a good ability to classify benign and malignant pulmonary nodules with an accuracy of 90.96%.