Research on image classification of pulmonary nodules based on improved EfficientNet network
Aiming at the current computer-aided diagnosis of benign and malignant pulmonary nodules with low accuracy,high misdiagnosis rate and complex model,an improved EfficientNet network classification model for benign and malignant pulmonary nodules was proposed.First,the EC A module is integrated in the feature extraction part,and the EMBConv structure is built to make the network model focus on more feature information.Secondly,the cross-domain transfer learning is used to improve the classification of the network model performance.Then the Ranger optimizer is used to optimize the network training to effectively prevent the model from falling into local optimum.Finally,the lung nodule images extracted from the LIDC-IDRI dataset are input into the improved classification model.The experimental results show that the proposed method shows strong competitiveness in the amount of network parameters and calculations,and the classification accuracy and precision have reached 91.83％and 95.50％,respectively,compared with the model before the improvement 1.66％and 4.41％.
pulmonary nodule classificationECA modulecross-domain transfer learningRanger optimizer
NETL
NSTL
万方数据
