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Estatística
Título: DEEP LEARNING MODEL FOR CLASSIFICATION OF TOMOGRAPHIC IMAGES OF POST-COVID PATTERNS
Autor(es): JOAO VICTOR ROCHA DA S M CERQUEIRA
Colaborador(es): MARLEY MARIA BERNARDES REBUZZI VELLASCO - Orientador
KARLA TEREZA FIGUEIREDO LEITE - Coorientador
Catalogação: 06/SET/2022 Língua(s): PORTUGUESE - BRAZIL
Tipo: TEXT Subtipo: SENIOR PROJECT
Notas: [pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio.
[en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio.
Referência(s): [pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/TFCs/consultas/conteudo.php?strSecao=resultado&nrSeq=60501@1
[de] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/TFCs/consultas/conteudo.php?strSecao=resultado&nrSeq=60501@5
DOI: https://doi.org/10.17771/PUCRio.acad.60501
Resumo:
In this work, a classification model of tomographic images linked to post-COVID or long-term COVID sequelae was implemented, with the aim of helping doctors diagnose such patterns. The model was implemented using a Convolutional Neural Networks algorithm with Transfer Learning in Python language, using Google Colaboratory which is a free cloud service hosted by Google and PyTorch frameworks. The models developed from post-covid patterns, identified in HUPE patients, showed very promising results.
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