@InProceedings{Backes:2022:PaImCl,
               author = "Backes, Andr{\'e} Ricardo",
          affiliation = "School of Computer Science, Federal University of 
                         Uberl{\^a}ndia",
                title = "Pap-smear image classification by using a fusion of texture 
                         features",
            booktitle = "Proceedings...",
                 year = "2022",
         organization = "Conference on Graphics, Patterns and Images, 35. (SIBGRAPI)",
             keywords = "texture analysis, PSO, pap-smear, image classification.",
             abstract = "In this paper we address the problem of pap-smear image 
                         classification. These images have great medical importance to 
                         diagnose and prevent uterine cervix cancer and have been 
                         intensively studied in computer vision research. We evaluated 19 
                         texture features on their ability to discriminate between two 
                         classes (normal and abnormal) of pap-smear images. We performed 
                         the classification of these feature using three different 
                         approaches: K-Nearest Neighbors (KNN), Support Vector Machine 
                         (SVM) and Linear Discriminant Data (LDA). We conducted this 
                         evaluation considering each texture method independently and their 
                         concatenation with others. Results show combining methods improves 
                         the accuracy, surpassing most of the compared methods, including 
                         some deep learning approaches.",
  conference-location = "Natal, RN",
      conference-year = "24-27 Oct. 2022",
                  doi = "10.1109/SIBGRAPI55357.2022.9991771",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI55357.2022.9991771",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/47JU645",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/47JU645",
           targetfile = "backes_16.pdf",
        urlaccessdate = "2024, May 02"
}