A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics

https://doi.org/10.1016/j.jbi.2015.05.011Get rights and content
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Highlights

  • A weighted rule based classification method for predicting malignancy of pulmonary nodules is proposed.

  • We benefit from nodule characteristics to predict malignancy.

  • Ensemble classifiers and dataset balancing methods are used for unbalanced data.

  • Results are compared with single classifiers that trained with image features.

  • Nodule characteristics can be used to improve classification accuracy on malignancy prediction.

Abstract

Predicting malignancy of solitary pulmonary nodules from computer tomography scans is a difficult and important problem in the diagnosis of lung cancer. This paper investigates the contribution of nodule characteristics in the prediction of malignancy. Using data from Lung Image Database Consortium (LIDC) database, we propose a weighted rule based classification approach for predicting malignancy of pulmonary nodules. LIDC database contains CT scans of nodules and information about nodule characteristics evaluated by multiple annotators. In the first step of our method, votes for nodule characteristics are obtained from ensemble classifiers by using image features. In the second step, votes and rules obtained from radiologist evaluations are used by a weighted rule based method to predict malignancy. The rule based method is constructed by using radiologist evaluations on previous cases. Correlations between malignancy and other nodule characteristics and agreement ratio of radiologists are considered in rule evaluation. To handle the unbalanced nature of LIDC, ensemble classifiers and data balancing methods are used. The proposed approach is compared with the classification methods trained on image features. Classification accuracy, specificity and sensitivity of classifiers are measured. The experimental results show that using nodule characteristics for malignancy prediction can improve classification results.

Keywords

Nodule characteristic
Ensemble classifier
Rule based classification
Unbalanced data

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