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We aimed to evaluate computer-aided diagnosis (CADx) system for lung noduleclassification focusing on (i) usefulness of gradient tree boosting (XGBoost)and (ii) effectiveness of parameter optimization using Bayesian optimization(Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lungcancers and 37 benign lung nodules) were included from public databases of CTimages. A variant of local binary pattern was used for calculating featurevectors. Support vector machine (SVM) or XGBoost was trained using the featurevectors and their labels. TPE or random search was used for parameteroptimization of SVM and XGBoost. Leave-one-out cross-validation was used foroptimizing and evaluating the performance of our CADx system. Performance wasevaluated using area under the curve (AUC) of receiver operating characteristicanalysis. AUC was calculated 10 times, and its average was obtained. The bestaveraged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both wereobtained using TPE. XGBoost was generally superior to SVM. Optimal parametersfor achieving high AUC were obtained with fewer numbers of trials when usingTPE, compared with random search. In conclusion, XGBoost was better than SVMfor classifying lung nodules. TPE was more efficient than random search forparameter optimization.

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization