Ensemble of Patches for COVID-19 X-Ray Image Classification

Autor(es)

Chen, Thiago Dong ; de Oliveira, Gabriel Bianchin ; Dias, Zanoni ; Rocha, AP ; Steels, L ; VandenHerik, J

• Tipo de documento: Artigo Científico
• Fonte: ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 3; v. N/A, p. 7-pg., 2022-01-01.

Resumo

With the COVID-19 pandemic, several efforts have been made to develop quick and effective diagnoses to assist health professionals in decision-making. In this work, we employed convolutional neural networks to classify chest radiographic images of patients between normal, pneumonia, and COVID-19. We evaluated the division of the images into patches, followed by the ensemble between the specialist networks in each of the image's parts. As a result, our classifier reached 90.67% in the test, surpassing another method in the literature. (AU)

• Processo FAPESP: 15/11937-9 - Investigação de problemas difíceis do ponto de vista algorítmico e estrutural
• Beneficiário: Flávio Keidi Miyazawa
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade
• Beneficiário: Anderson de Rezende Rocha
• Modalidade de apoio: Auxílio à Pesquisa - Temático