Benign and Malignant Solitary Pulmonary Nodules Classification Based on CNN and SVM

Abstract

In order to assist the doctors to diagnose lung cancer and improve the classification accuracy of benign and malignant pulmonary nodules, this paper proposes a novel intelligent diagnosis model which is aiming at CT imaging features of pulmonary nodules. Specifically, this model uses the convolutional neural network to extract the features of the pulmonary nodules, then uses the principal component analysis to reduce the dimension of the extracted features, and finally classifies the final features with particle swarm optimization optimized SVM. With regard to the pulmonary nodules extracted from the LIDC-IDRI database, 400 pulmonary nodules are used for training and 310 pulmonary nodules are used for testing, the classification accuracy rate is 91.94%. This model can provide objective, convenient and efficient auxiliary method for solving the classification problem of benign and malignant pulmonary nodules in medical images.