@InProceedings{SilvaAngeSantLoul:2021:MoCo,
author = "Silva, Joacy Mesquita da and Angelo, Michele F{\'u}lvia and
Santos, Washington L. C. dos and Loula, Angelo C",
affiliation = "{Universidade Estadual de Feira de Santana (UEFS)} and
{Universidade Estadual de Feira de Santana (UEFS)} and
{Funda{\c{c}}{\~a}o Oswaldo Cruz - Instituto Gon{\c{c}}alo
Moniz} and {Universidade Estadual de Feira de Santana (UEFS)}",
title = "Aprendizado Profundo na Classifica{\c{c}}{\~a}o de Les{\~o}es
Crescentes Glomerulares: modelos e condi{\c{c}}{\~o}es",
booktitle = "Proceedings...",
year = "2021",
editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and
Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario
and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos,
Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira,
Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir
A. and Fernandes, Leandro A. F. and Avila, Sandra",
organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "deep learning, glomerular crescent, nephropathology.",
abstract = "Glomeruli are structures in the kidneys, responsible for filtering
the blood, that can be affected by several lesions, such as the
glomerular crescent, which is characterized by abnormal cell
proliferation. In this work, different models and conditions for
the application of deep learning are to evaluated in the task of
classifying glomerular crescent histopathological images. The
pre-trained networks Xception, InceptionV3, MobileNet, VGG16 and
ResNet50 were compared, by applying to the classification of
images with crescent vs normal glomeruli. Comparing the accuracy,
precision, recall and f1-score of the models, the ResNet50 showed
significantly better performance than the other networks, in all
measures. The application of data augmentation did not result in a
significant improvement in the results in this case. In an
experiment of classification of crescent vs non-crescent
glomeruli, adding images of three other lesions to the database,
the application of Focal Loss presented greater accuracy and
precision.",
conference-location = "Gramado, RS, Brazil (virtual)",
conference-year = "18-22 Oct. 2021",
language = "pt",
ibi = "8JMKD3MGPEW34M/45F7NEB",
url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45F7NEB",
targetfile = "
Artigo_WIP_SIBGRAPI_Aprendizado_Profundo_na_Classifica__o_de_Les_es_Crescentes_Glomerulares__modelos_e_condi__es.pdf",
urlaccessdate = "2024, May 03"
}