Classification of Benign and Malignant Pulmonary Nodules Based on the Multiresolution 3D DPSECN Model and Semisupervised Clustering

Deep learning model training requires a large number of labeled samples, but the acquisition of labeled samples is time-consuming and laborious in the medical field. To solve this problem, a semisupervised clustering algorithm combined with a 3D convolutional neural network model is proposed to improve the classification performance for benign and malignant pulmonary nodules. The research contents are as follows: Firstly, a multiresolution 3D dual path squeeze excitation deep learning network model is constructed. Then, the feature extractor in the network model is used to extract the high-level features of the image, and semisupervised clustering is applied to the extracted image features. The corresponding pseudolabels can be obtained for the unlabeled samples, and the categories of unlabeled samples are determined and utilized. Finally, the oversampling algorithm is used to balance the data categories of different types of samples, and the benign and malignant pulmonary nodules are classified by a classifier constructed by a 3D dual path squeeze excitation network. The experimental results show that the proposed semisupervised clustering algorithm can label the categories of unlabeled samples. The proposed network model can learn more characteristics related to pulmonary nodules and can effectively improve the classification performance of pulmonary nodules. The proposed network model was tested using Lung Image Database Consortium (LIDC-IDRI) dataset, and an accuracy of 94.4% and an AUC of 0.931 were obtained. Compared with some existing classification models, the proposed method can achieve a better classification effect of pulmonary nodules.