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

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.Comment: 29 pages, 4 figure

Effects of different silica intermediate layers for hydrogen diffusion enhancement of palladium membranes applied to porous stainless steel support

Porous stainless steel (SUS) supports were modified with double intermediate layers, silicalite-1 and γ-alumina, to enhance the hydrogen diffusion of a thin palladium membrane. One of layers, silicalite-1, was prepared using the hydrothermal synthetic method on porous SUS supports. The differences in expansion/contraction behaviors caused by different thermal coefficients of expansion between silicalite-1 and the SUS resulted in a lowering of the durability of the membrane. Intermediates layers of mesoporous MCM-48 powders or commercial spherical non-porous silica particles were then applied to porous SUS supports via aspiration, γ-alumina was introduced by dip-coating, and the Pd membrane was subjected to electro-less plating. H2 permeance of the Pd membrane (membrane thickness: 11 μm) containing spherical silica particles was around 10 × 10−6 mol·m−2·s−1·Pa−1 at 600 °C, which was higher than that of the Pd membrane (membrane thickness: 7 μm) containing MCM-48. The durability of the Pd membrane containing spherical silica particles was higher than that of the version containing MCM-48 powders. These results suggest that commercial spherical non-porous silica particles will uniformly occupy the pores of the SUS tubes and enhance the H2 permeance and durability of the Pd membrane