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