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

TL;DR

This study evaluates a computer-aided diagnosis system for lung nodule classification, demonstrating that XGBoost combined with Bayesian optimization (TPE) outperforms SVM and random search in accuracy and efficiency.

Contribution

It introduces the use of gradient tree boosting with Bayesian optimization for lung nodule classification, showing improved performance and parameter tuning efficiency.

Findings

XGBoost achieved higher AUC than SVM.

Bayesian optimization with TPE was more efficient than random search.

XGBoost with TPE achieved the best average AUC of 0.896.

Abstract

We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule classification 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 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of local binary pattern was used for calculating feature vectors. Support vector machine (SVM) or XGBoost was trained using the feature vectors and their labels. TPE or random search was used for parameter optimization of SVM and XGBoost. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. In conclusion, XGBoost was better than SVM for classifying lung nodules. TPE was more efficient than random search for parameter optimization.