Lung Nodule Classification Using Supervised Manifold Learning Based on All-Class

Dimensionality reduction plays an important role in lung nodule classification, but in most of the existing methods, dimensionality is reduced with all classes being considered jointly, difference between feature subsets of different classes is ignored. In this paper, a supervised manifold feature extraction method based on fusion of all-class and pair wise-class is proposed. Firstly, a manifold learning method will be improved with category information being used fully. Secondly, features will be extracted by the improved manifold learning method and the feature subsets is divided into several parts, one is based on all-class structure, others based on each pair of classes. Finally, All-class subset is used in K-nearest (KNN) classifiers, others used in Support Vector Machine (SVM) classifiers, and an supervised multi-classifiers system of lung nodule classification is constructed. Experiments show a significant improvement in recognition accuracy.