Improved Machine Learning Model in the Classification of Benign and Malignant Lung Nodules
With the increasing incidence of lung cancer,rapid early assessment of lung nodules by means of imaging is of great significance to improve the quality of life of patients.To solve this problem,a new classification model of benign and malignant pulmonary nodules is proposed.Firstly,the model adopts the oversampling technique to eliminate the result deviation caused by the high proportion of benign samples.Then,the image omics features of each nodule are extracted,and the optimal feature subset is se-lected by using spearman correlation variable elimination and minimum absolute contraction selection operator.Finally,by using the inertia weight of decreasing cosine,the random generation distributed delayed particle swarm optimization algorithm is improved to search the global optimal parameters accurately and establish the best classification model.The model is trained and tested on 608 training sets and 68 test sets on LIDC database.The AUC,accuracy,precision and recall rates of the model on the test set are 0.93,0.941,0.917 and 0.971,respectively.The results show that this method can classify pulmonary nodules more effectively and is ex-pected to be used in clinical diagnosis of benign and malignant pulmonary nodules.
万方数据
维普
