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

Thiago Chen, Gabriel Oliveira, Zanoni Dias

2022

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.

Paper Citation

in Harvard Style

Chen T., Oliveira G. and Dias Z. (2022). Ensemble of Patches for COVID-19 X-Ray Image Classification. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 561-567. DOI: 10.5220/0010864500003116

in Bibtex Style

@conference{icaart22,
author={Thiago Chen and Gabriel Oliveira and Zanoni Dias},
title={Ensemble of Patches for COVID-19 X-Ray Image Classification},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={561-567},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010864500003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Ensemble of Patches for COVID-19 X-Ray Image Classification
SN - 978-989-758-547-0
AU - Chen T.
AU - Oliveira G.
AU - Dias Z.
PY - 2022
SP - 561
EP - 567
DO - 10.5220/0010864500003116