@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 = "Porto de Galinhas (virtual)",
conference-year = "7-10 Nov. 2020",
doi = "10.1109/SIBGRAPI51738.2020.00029",
url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00029",
language = "en",
ibi = "8JMKD3MGPEW34M/43AH6HP",
url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43AH6HP",
targetfile = "Paper 43.pdf",
urlaccessdate = "2025, Feb. 16"
}