| Full text | |
| Author(s): |
Chen, Thiago Dong
;
de Oliveira, Gabriel Bianchin
;
Dias, Zanoni
;
Rocha, AP
;
Steels, L
;
VandenHerik, J
Total Authors: 6
|
| Document type: | Journal article |
| Source: | ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 3; v. N/A, p. 7-pg., 2022-01-01. |
| Abstract | |
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) | |
| FAPESP's process: | 15/11937-9 - Investigation of hard problems from the algorithmic and structural stand points |
| Grantee: | Flávio Keidi Miyazawa |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Projects - Thematic Grants |