Predicting @entity5474 from computer tomography scans is a difficult and important problem in the diagnosis of @entity2 . This paper investigates the contribution of nodule characteristics in the prediction of @entity5 . Using data from Lung Image Database Consortium (LIDC) database, we propose a weighted rule based classification approach for predicting @entity358 . 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 @entity5 . The rule based method is constructed by using radiologist evaluations on previous cases. Correlations between @entity5 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 @entity5 prediction can improve classification results.

A weighted rule based method for predicting XXXX by nodule characteristics.

@entity5474 :: ('MESH:D003074', 'Disease') :: ['malignancy of solitary pulmonary nodules'] @entity2 :: ('MESH:D008175', 'Disease') :: ['lung cancer'] @entity5 :: ('MESH:D009369', 'Disease') :: ['malignancy'] @entity358 :: ('MESH:D008171', 'Disease') :: ['malignancy of pulmonary nodules']
@entity358 :: (MESH:D008171,Disease) :: ['malignancy of pulmonary nodules']
