@InProceedings{FelixFerrOlivMach:2016:Us3DTe,
               author = "Felix, Ailton de Lima Filho and Ferreira Junior, Jos{\'e} Raniery 
                         and Oliveira, Marcelo Costa and Machado, Aydano Pamponet",
          affiliation = "{Universidade Federal de Alagoas} and {Universidade de S{\~a}o 
                         Paulo} and {Universidade Federal de Alagoas} and {Universidade 
                         Federal de Alagoas}",
                title = "Using 3D Texture and Margin Sharpness Features on Classification 
                         of Small Pulmonary Nodules",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "lung cancer, small nodules, early diagnosis, computer-aided 
                         diagnosis, texture features, margin sharpness features, 
                         classification, machine learning.",
             abstract = "The lung cancer is the reason of a lot of deaths on population 
                         around the world. An early diagnosis brings a most curable and 
                         simpler treatment options. Due to complexity diagnosis of small 
                         pulmonary nodules, Computer-Aided Diagnosis (CAD) tools provides 
                         an assistance to radiologist aiming the improvement in the 
                         diagnosis. Extracting relevant image features is of great 
                         importance for these tools. In this work we extracted 3D Texture 
                         Features (TF) and 3D Margin Sharpness Features (MSF) from the Lung 
                         Image Database Consortium (LIDC) in order to create a 
                         classification model to classify small pulmonary nodules with 
                         diameters between 3-10mm. We used three machine learning 
                         algorithm: k-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP) 
                         and Random Forest (RF). These algorithms were trained by different 
                         set of features from the TF and MSF. The classification model with 
                         MLP algorithm using the selected features from the integration of 
                         TF and MSF achieved the best AUC of 0.820.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.061",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.061",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M4UBD2",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M4UBD2",
           targetfile = "PID4357869.pdf",
        urlaccessdate = "2024, May 02"
}