A classification framework for lung tissue categorization
Adrien Depeursinge, Jimison Iavindrasana, Asmâa Hidki, Gilles Cohen, Antoine Geissbühler, Alexandra Platon, Pierre‐Alexandre Poletti, Henning Müller · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
We compare five common classifier families in their ability to categorize six lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) but also normal tissue. The evaluated classifiers are Naive Bayes, k-Nearest Neighbor (k-NN), J48 decision trees, Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). The dataset used contains 843 regions of interest (ROI) of healthy and five pathologic lung tissue patterns identified by two radiologists at the University Hospitals of Geneva. Correlation of the feature space composed of 39 texture attributes is studied. A grid search for optimal parameters is carried out for each classifier family. Two complementary metrics are used to characterize the performances of classification. Those are based on McNemar's statistical tests and global accuracy. SVM reached best values for each metric and allowed a mean correct prediction rate of 87.9% with high class-specific precision on testing sets of 423 ROIs.