Feature definition, analysis and selection for lung nodule classification in chest computerized tomography images

dc.contributor.author Gonçalves,L en
dc.contributor.author Novo,J en
dc.contributor.author Aurélio Campilho en
dc.date.accessioned 2018-01-06T16:36:09Z
dc.date.available 2018-01-06T16:36:09Z
dc.date.issued 2016 en
dc.description.abstract This work presents the results of the characterization of lung nodules in chest Computerized Tomography for benign/malignant classification. A set of image features was used in the Computer-aided Diagnosis system to distinguish benign from malignant nodules and, therefore, diagnose lung cancer. A filter-based feature selection approach was used in order to define an optimal subset with higher accuracy. A large and heterogeneous set of 293 features was defined, including shape, intensity and texture features. We used different KNN and SVM classifiers to evaluate the features subsets. The estimated results were tested in a dataset annotated by radiologists. Promising results were obtained with an area under the Receiver Operating Characteristic curve (AUC value) of 96:2 ± 0:5% using SVM. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/5657
dc.language eng en
dc.relation 6071 en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title Feature definition, analysis and selection for lung nodule classification in chest computerized tomography images en
dc.type conferenceObject en
dc.type Publication en
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