A Multiclassifier Approach for Lung Nodule Classification

Abstract

The aim of this paper is to examine a multiclassifier approach to the classification of the lung nodules in X-ray chest radiographs. The approach investigated here is based on an image region-based classification whose output is the information of the presence or absence of a nodule in an image region. The classification was made, essentially, in two steps: firstly, a set of rotation invariant features was extracted from the responses of a multi-scale and multi-orientation filter bank; secondly, different classifiers (multi-layer perceptrons) are designed using different features sets and trained in different data. These classifiers are further combined in order to improve the classification performance. The obtained results are promising and can be used for reducing the false-positives nodules detected in a computer-aided diagnosis system.KeywordsHide LayerPulmonary NoduleImage RegionLung NoduleNodule CandidateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.