The current computer aided diagnosis system classifies benign or malignant lung nodules mainly according to the low-level features of lung CT images.However

clinicians use the high-level semantic features of lung CT images.To overcome the inconsistency between the low-level features and high-level semantic features

a new approach of benign or malignant lung nodules classification based on semantic attributes is proposed.Firstly

lung nodule images are extracted using the threshold probability-map method.Secondly

on the one hand

some features including shape

gray

texture

size and position are extracted from lung nodule images to constitute the low-level feature set;on the other hand

according to the experts' annotation of lung nodules

the attributes are extracted to constitute the high-level attribute set.Thirdly

attribute prediction models are built to map the low-level features to the high-level attributes.Finally

the benign or malignant classification of lung nodules is performed using the predicted attributes.Experimental results on the LIDC dataset show that the proposed classification method possesses high classification accuracy and AUC value.