Predicting the Semantic Characteristics of Pulmonary Nodules using Feature Selection Based on Maximum-relevance Minimum-redundancy

Computer-aided diagnosis (CAD) is mainly used in disease diagnosis and cause analysis. For example, using CAD to make early predictions of the semantic features of lung nodules is critical for helping physicians judge the semantic features of solitary pulmonary nodules. It is an effective method to predict disease using the features calculated from CT images. But how to select the most relevant features from the large number of image features is still a challenge. In this paper, we perform feature selection using maximum-relevance minimum-redundancy criteria based on applying a support vector machine (MRMR_SVM) on four types of computed image features to predict the semantic characteristics of pulmonary nodules over seven categories. The proposed method has the following advantages. 1) It improves work efficiency and reduces costs compared to manual evaluation by radiologists. 2) It combines a few key image features with specific semantic features to provide a basis for radiologists' diagnosis. 3) It eliminates noisy data and improves accuracy of early prediction of pulmonary nodules compared to using all features. The experimental results show that the proposed method performs well at predicting the semantic features of lung nodules in terms of accuracy and running time.