Lung nodule classification using local kernel regression models with out-of-sample extension

Abstract Computer-aided classification is a major research task for computer-aided diagnosis of pulmonary nodules. In radiology domain, labeled data can be expensive to generate. Therefore, in this study, a novel unsupervised spectral clustering algorithm was presented to distinguish benign and malignant nodules. In this algorithm, a new Laplacian matrix was constructed by using local kernel regression models (LKRM) and incorporating a regularization term, the regularization term can tackle the out-of-sample problem. To verify the feasibility of our algorithm, a ground truth dataset was assembled from the LIDC-IDRI database, including 371 benign and 375 malignant lung nodules. All nodules were represented by the texture features, which were computed from the regions of interest (ROIs). Extensive experiments on lung nodules showed that the proposed algorithm not only achieved a higher classification performance than existing popular unsupervised algorithms, but also had superiority comparing to some supervised algorithms (linear discriminant analysis and extreme learning machine).

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