The algorithm was evaluated on a dataset consisting of 51 UIP and 56 normal cases, a combined feature vector was computed for each case and an SVM classifier (RBF kernel) was used to classify them into UIP or normal using ten-fold cross validation. A receiver operating characteristic (ROC) area under the curve (AUC) was used for evaluation. The highest AUC of 0.95 was achieved by using concatenated features and an N of 27. Using lung partition (N = 27, 64) with concatenated features had significantly better result over not using partitions (N = 1) (p-value < 0.05). Therefore this equal-volume partition fractional high-density volume method is useful in distinguishing early-stage UIP from normal cases.
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Yiting Xie, Mary Salvatore, Shuang Liu, Artit Jirapatnakul, David F. Yankelevitz, Claudia I. Henschke, Anthony P. Reeves, "Identification of early-stage usual interstitial pneumonia from low-dose chest CT scans using fractional high-density lung distribution," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013408 (3 March 2017);