Close
Metadata

@InProceedings{Paz-SotoHeroFernDíaz:2020:AuClEr,
               author = "Paz-Soto, Yaima and Herold-Garcia, Silena and Fernandes, Leandro 
                         A. F. and D{\'{\i}}az-Matos, Saul",
          affiliation = "{Universidad de Gu{\'a}ntanamo} and {Universidad de Oriente} and 
                         {Universidade Federal Fluminense} and {Universidad de Oriente}",
                title = "Automatic Classification of Erythrocytes Using Artificial Neural 
                         Networks and Integral Geometry-Based Functions",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "sickle cell disease, integral geometry, artificial neural 
                         networks, shape descriptor, classification.",
             abstract = "The red blood cell deformation caused by disorders like sickle 
                         cell disease can be assessed by observing blood samples under a 
                         microscope. This manual process is cumbersome and prone to errors 
                         but can be supported by automated techniques that allow red blood 
                         cells to be classified according to the shape they present. There 
                         are proposals in the literature that use functions based on 
                         integral geometry to obtain a description of the cells' contour 
                         before performing classification, reaching 96.16% accuracy with 
                         the use of the k-Nearest Neighbor (KNN) classifier. In those 
                         approaches, the classification-confusion cases persist mainly in 
                         the classes of most significant interest, which are those related 
                         to the detection of deformed cells. In this work, we use 
                         artificial neural networks-based classifiers, trained with the 
                         characteristics obtained from integral geometry-based functions, 
                         to classify erythrocytes into normal, sickle, and other 
                         deformations classes. Our proposal achieves accuracy of 98.40%. 
                         This result is superior to those of previous studies concerning 
                         the classes of greatest interest. Also, our approach is 
                         computationally more efficient than previous works, making it 
                         suitable for supporting medical follow-up diagnosis of sickle cell 
                         disease.",
  conference-location = "Virtual",
      conference-year = "Nov. 7-10, 2020",
             language = "en",
           targetfile = "Paper 43.pdf",
        urlaccessdate = "2022, Jan. 21"
}


Close