Please use this identifier to cite or link to this item:
|Title:||Feature definition, analysis and selection for lung nodule classification in chest computerized tomography images|
|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.|
|Appears in Collections:||C-BER - Articles in International Conferences|
Files in This Item:
|1.77 MB||Adobe PDF|
View/Open Request a copy
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.