Aiming at the lack of quantitative evaluation methods in clinical diagnosis of lung cancer, a classification and prediction model of lung cancer based on Support Vector Machine (SVM) was constructed by using radiomics method. Firstly, the definition and processing flow of radiomics were introduced. The experimental samples were selected from 816 lung cancer patients on LIDC. Firstly, ROI was extracted by central pooling convolution neural network segmentation method. Then, Pyradiomics and FSelector feature selection models were used to extract features and reduce dimension. Finally, SVM was used to construct the classification and prediction model of lung tumors. The predictive accuracy of the model is 80.4% for the classification of benign and malignant pulmonary nodules larger than 5 mm, and the value of the area under the curve (AUC) is 0.792. This indicates that the SVM classifier model can accurately distinguish benign and malignant pulmonary nodules larger than 5 mm.
Algorithms ; Humans ; Lung Neoplasms/diagnostic imaging* ; Neural Networks, Computer ; Radiometry ; Support Vector Machine ; Tomography, X-Ray Computed