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Reference TypeConference Paper (Conference Proceedings)
Last Update2021: (UTC)
Metadata Last Update2021: (UTC) administrator
Citation KeySilvaAngeSantLoul:2021:MoCo
TitleAprendizado Profundo na Classificação de Lesões Crescentes Glomerulares: modelos e condições
Access Date2022, Jan. 22
Number of Files1
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Author1 Silva, Joacy Mesquita da
2 Angelo, Michele Fúlvia
3 Santos, Washington L. C. dos
4 Loula, Angelo C
Affiliation1 Universidade Estadual de Feira de Santana (UEFS)
2 Universidade Estadual de Feira de Santana (UEFS)
3 Fundação Oswaldo Cruz - Instituto Gonçalo Moniz
4 Universidade Estadual de Feira de Santana (UEFS)
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado (Virtual), Brazil
DateOctober 18th to October 22nd, 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeWork in Progress
History (UTC)2021-09-20 13:36:18 :: -> administrator ::
2021-11-12 11:47:13 :: administrator -> :: 2021
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsdeep learning
glomerular crescent
AbstractGlomeruli 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. > SDLA > SIBGRAPI 2021 > Aprendizado Profundo na...
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Target FileArtigo_WIP_SIBGRAPI_Aprendizado_Profundo_na_Classifica__o_de_Les_es_Crescentes_Glomerulares__modelos_e_condi__es.pdf
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