<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>sibgrapi.sid.inpe.br 802</site>
		<identifier>8JMKD3MGPEW34M/45F7NEB</identifier>
		<repository>sid.inpe.br/sibgrapi/2021/09.20.13.36</repository>
		<lastupdate>2021:09.20.13.36.18 sid.inpe.br/banon/2001/03.30.15.38 angelocl@gmail.com</lastupdate>
		<metadatarepository>sid.inpe.br/sibgrapi/2021/09.20.13.36.18</metadatarepository>
		<metadatalastupdate>2021:11.12.11.47.13 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2021}</metadatalastupdate>
		<citationkey>SilvaAngeSantLoul:2021:MoCo</citationkey>
		<title>Aprendizado Profundo na Classificação de Lesões Crescentes Glomerulares: modelos e condições</title>
		<format>On-line</format>
		<year>2021</year>
		<numberoffiles>1</numberoffiles>
		<size>1505 KiB</size>
		<author>Silva, Joacy Mesquita da,</author>
		<author>Angelo, Michele Fúlvia,</author>
		<author>Santos, Washington L. C. dos,</author>
		<author>Loula, Angelo C,</author>
		<affiliation>Universidade Estadual de Feira de Santana (UEFS)</affiliation>
		<affiliation>Universidade Estadual de Feira de Santana (UEFS)</affiliation>
		<affiliation>Fundação Oswaldo Cruz - Instituto Gonçalo Moniz</affiliation>
		<affiliation>Universidade Estadual de Feira de Santana (UEFS)</affiliation>
		<editor>Paiva, Afonso,</editor>
		<editor>Menotti, David,</editor>
		<editor>Baranoski, Gladimir V. G.,</editor>
		<editor>Proença, Hugo Pedro,</editor>
		<editor>Junior, Antonio Lopes Apolinario,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Pagliosa, Paulo,</editor>
		<editor>dos Santos, Thiago Oliveira,</editor>
		<editor>e Sá, Asla Medeiros,</editor>
		<editor>da Silveira, Thiago Lopes Trugillo,</editor>
		<editor>Brazil, Emilio Vital,</editor>
		<editor>Ponti, Moacir A.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Avila, Sandra,</editor>
		<e-mailaddress>angelocl@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)</conferencename>
		<conferencelocation>Gramado (Virtual), Brazil</conferencelocation>
		<date>October 18th to October 22nd, 2021</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Work in Progress</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>deep learning,  glomerular crescent, nephropathology.</keywords>
		<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.</abstract>
		<language>pt</language>
		<targetfile>Artigo_WIP_SIBGRAPI_Aprendizado_Profundo_na_Classifica__o_de_Les_es_Crescentes_Glomerulares__modelos_e_condi__es.pdf</targetfile>
		<usergroup>angelocl@gmail.com</usergroup>
		<visibility>shown</visibility>
		<mirrorrepository>sid.inpe.br/banon/2001/03.30.15.38.24</mirrorrepository>
		<nexthigherunit>8JMKD3MGPEW34M/45PQ3RS</nexthigherunit>
		<hostcollection>sid.inpe.br/banon/2001/03.30.15.38</hostcollection>
		<username>angelocl@gmail.com</username>
		<agreement>agreement.html .htaccess .htaccess2</agreement>
		<lasthostcollection>sid.inpe.br/banon/2001/03.30.15.38</lasthostcollection>
		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2021/09.20.13.36</url>
	</metadata>
</metadatalist>